WO2022048804A1 - Monitoring for a synthetic lengthy body - Google Patents

Monitoring for a synthetic lengthy body Download PDF

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Publication number
WO2022048804A1
WO2022048804A1 PCT/EP2021/065178 EP2021065178W WO2022048804A1 WO 2022048804 A1 WO2022048804 A1 WO 2022048804A1 EP 2021065178 W EP2021065178 W EP 2021065178W WO 2022048804 A1 WO2022048804 A1 WO 2022048804A1
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WO
WIPO (PCT)
Prior art keywords
synthetic
ray
data
wear
lengthy body
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Application number
PCT/EP2021/065178
Other languages
French (fr)
Inventor
Matthias ARRAS
Luigi BALZANO
Mathias Kasper HUSS-HANSEN
Matti Knaapila
Aksel Sylvest OBDRUP
Original Assignee
Dsm Ip Assets B.V.
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Application filed by Dsm Ip Assets B.V. filed Critical Dsm Ip Assets B.V.
Publication of WO2022048804A1 publication Critical patent/WO2022048804A1/en

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/20—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by using diffraction of the radiation by the materials, e.g. for investigating crystal structure; by using scattering of the radiation by the materials, e.g. for investigating non-crystalline materials; by using reflection of the radiation by the materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N17/00—Investigating resistance of materials to the weather, to corrosion, or to light

Definitions

  • the invention relates to a computer implemented monitoring method for a synthetic lengthy body, a computer implemented training method for a monitoring system, a monitoring device for a synthetic lengthy body, a load lifting device, a training system for a synthetic lengthy body monitoring system, and a computer readable medium.
  • a synthetic lengthy body such as a rope is especially adapted to be used as load-bearing element in many applications, e.g., as lifting ropes.
  • bending may occur repeatedly, for example, over a sheave during a lifting job.
  • a rope wears down, e.g., experiences wear, and may fail due to rope and/or filament damage.
  • Such fatigue failure is often referred to as bend fatigue or flex fatigue.
  • wear exceeds a certain level the synthetic lengthy body may need to be discontinued, for fear it may fail, e.g., break.
  • Synthetic lengthy body herein comprises synthetic filaments.
  • An advantageous material choice for a synthetic lengthy body, such as a rope, is polyethylene (PE), in particular ultra-high-molecular-weight polyethylene (UHMWPE).
  • PE polyethylene
  • UHMWPE ultra-high-molecular-weight polyethylene
  • UHMWPE ropes which herein are ropes made using UHMWPE filaments
  • UHMWPE filaments are emerging as a strong and light-weight alternative to steel wire ropes in many areas such as heavy lifting, maritime applications, commercial fishing, aquaculture, wind, defence and deep sea operations.
  • the condition of ropes and wires are regularly inspected to ensure safe operations.
  • steel wire ropes this is typically done using magnetic flux leakage techniques.
  • synthetic lenghty bodies, such as UHMWPE ropes are subject to wear, which is critical to detect.
  • the International Organization for Standardization (ISO) and the world’s largest classification society DNV- GL define the standards for off-shore fiber ropes describing current monitoring methods based on visual inspections and counting of loading cycles.
  • Japanese patent JP6048603 with title ‘Method for determining deterioration of colored polyethylene fiber, and colored polyethylene fiber’, included herein by reference, provides a method to judge degradation of a polyethylene fiber.
  • the known method comprises coloring a polyethylene fiber and judging the deterioration of the fiber from an observed degradation of the coloring.
  • NDT non-destructive testing
  • a condition monitoring system for a synthetic lengthy body such as a rope comprising UHMWPE filaments, is provided.
  • the system may predict a remaining lifetime of the synthetic length body, e.g., a remaining lifetime for use in a particular application such as lifting objects.
  • the system may generate a warning signal if the level of wear exceeds a pre-set wear threshold.
  • diffractive X-ray data measured at a synthetic lengthy body contains information that correlates with the wear history of a said body, such as the wear history of a filament, a strand and/or a rope; in particular a synthetic lengthy body comprising UHMWPE, in particular made from UHMWPE.
  • a synthetic lengthy body comprising UHMWPE, in particular made from UHMWPE.
  • the system provides a quantitative model linking the crystalline morphology of the synthetic filaments in the synthetic lengthy body to mechanical properties of the rope.
  • X-ray data contains information from inside the material forming the synthetic length body, e.g., inside the synthetic filament, which is not possible to obtain from external inspection such as visual inspection or optical inspection.
  • X-ray allows to take microscopic phenomena into account, which is not possible for visual inspection or for optical methods.
  • X-ray diffraction data is integrative; this means that information is not recorded locally, e.g., on the surface, but captured in an accumulative fashion from the bulk. The information is obtained over the whole length that the X-ray beam goes through the material.
  • an X-ray inspection device may be installed in a physical location which is inconvenient or dangerous for a visual inspector.
  • Diffractive X-ray data is preferably Wide-angle X-ray scattering (WAXS) data.
  • the diffractive X-ray data may also be medium angle x-ray scattering (MAXS) data, or an Ultra-Wide-Angle X-Ray scattering, or a small angle X-ray scattering.
  • MAXS medium angle x-ray scattering
  • the Wide angle X-ray scattering (WAXS) data corresponds to a d-spacing between 0.15 nm to 0.7nm, in particular corresponds to a d-spacing in the range from 0.15 nm to 0.7nm.
  • Synthetic ropes such as ropes made from ultra-high-molecular-weight polyethylene (UHMWPE) filaments are replacing steel wire ropes in many applications and a non-destructive testing method to monitor their condition is of scientific and commercial interest.
  • UHMWPE ultra-high-molecular-weight polyethylene
  • the term rope and synthetic rope are used interchangably herein, if a steel wire rope is meant this is specifically named a steel wire rope.
  • WAXS wide-angle X-ray scattering
  • LDA linear discriminant analysis
  • WAXS wide-angle X-ray scattering
  • LDA linear discriminant analysis
  • a computer implemented monitoring method for a synthetic lengthy body in which diffractive X-ray data is obtained for a synthetic lengthy body.
  • the synthetic lengthy body may be a synthetic rope, a synthetic belt or a synthetic chain made using a synthetic material.
  • the synthetic material may be polyethylene, e.g., UHMWPE or aromatic polyamide, also referred to as aramid.
  • UHMWPE fibers are known under trademark Dyneema®
  • aramid fibers are known under trademarks Keylar® and Nomex®.
  • the diffractive X-ray data may be analyzed, and a value may be generated quantifying a level of wear in the corresponding part of the synthetic lengthy body. If the level of wear indicated by the value exceeds a wear threshold, then a warning signal may be generated.
  • the diffractive X-ray data depends on the crystalline/amorphous structure.
  • the diffracted X-ray radiation e.g., as represented in the diffractive X-ray data, provides information about crystalline morphology of the filaments.
  • the diffractive X-ray data is dimensional data, e.g., 1- dimensional or 2-dimensional. It is envisioned, that one could even use 3-dimensional X- ray diffraction tomography.
  • the diffractive X-ray data may be image data.
  • the diffraction image may be obtained from 1 D information, e.g., from a diffraction pattern, such as obtained from an array or linear detector or from 2D information.
  • the diffractive X-ray data may represent a diffraction image.
  • 2D diffractive X-ray data may include a diffraction pattern (non-reduced) or diffractogram (reduced, e.g., integrated to 1 D).
  • a diffraction image includes a diffraction pattern and a diffractogram.
  • wear such as intra-filament damage, changes a structural property of the filament. This changed property is correlated with the bending history of a synthetic lengthy body, such as a rope made from UHMWPE filaments, this allows to non-destructively classify levels of wear.
  • the external of a synthetic lengthy body is the outside, that is, that what you can see. Internal is inside the synthetic lengthy body, e.g., at strand, or filament level. Intra-filament wear results in a structural change inside the filament. It was not known in the art, how to quantify intra-filament wear using diffractive X-ray data.
  • a machine learnable model may be applied to the processed diffractive X-ray data, to generate a value quantifying a level of wear in the part of the synthetic lengthy body.
  • a machine learnable model may be applied to the processed diffractive X-ray data, to generate a value quantifying a level of wear in the part of the synthetic lengthy body.
  • the relationship between diffractive X-ray data and the condition of the rope is highly non-linear and non-obvious. Wear in a synthetic rope is the result of a complex interaction between the single filaments that make up the internal structure of the rope, including, e.g., frictional sliding, heat, torsion of filaments, and so on.
  • the state of the art does not provide quantitative models linking damage of the crystalline morphology to the decline in mechanical properties of the rope. It was not known in the art how to determine the condition of the rope given diffractive X-ray data. By applying a machine learnable model this correlation can be made explicit in a practical method.
  • a machine learnable model enables the use of large amounts of data of possibly inferior quality data. This in turns allows data to be collected in the field where conditions are not as well-defined as in a laboratory, and where the measurement time per point may be short.
  • a machine learnable model further allows integrating multiple information sources, e.g., multiple diffractive X-ray data, from different parts of the rope, and/or further sensor data obtained from a different sensor modality, e.g., visual data, such a images taken by a camera, in particular a high speed camera, of the outside of the rope; optical or spectroscopy data; acoustic data such as data from acoustic emission or an acoustic-ultrasonic method; rope diameter data or a combination thereof.
  • multiple information sources e.g., multiple diffractive X-ray data
  • sensor data obtained from a different sensor modality, e.g., visual data, such a images taken by a camera, in particular a high speed camera, of the outside
  • the present invention therefore also relates a computer implemented monitoring method for a synthetic lengthy body, comprising as additional step obtaining further sensor data from a further sensor, wherein the further sensor data is optical data, acoustic data, such as acoustic emission or an acoustic-ultrasonic method.
  • Embodiments may be adapted to rope diameter, also referred to as rope thickness herein, in various ways. For example, one may select a suitable X-ray energy, e.g., typically higher for thicker ropes. Depending on the energy a scattering setup and geometry may be selected, so that a d-spacing range can be obtained, this is then a range of lengths in real space. This is further described herein. The obtained data may then be analyzed, e.g., classified using a machine learnable model.
  • a suitable X-ray energy e.g., typically higher for thicker ropes.
  • a scattering setup and geometry may be selected, so that a d-spacing range can be obtained, this is then a range of lengths in real space. This is further described herein.
  • the obtained data may then be analyzed, e.g., classified using a machine learnable model.
  • the monitoring device is based on a machine learnable model with input data obtained via an acoustic-ultrasonic method.
  • the acousticultrasonic methods employ two ultrasonic/acoustics transducers where for example one is working as a transmitter the other as a receiver (detector).
  • the transmitter and the receiver are placed spatially apart (preferably between 10 and 50cm) along the length of lengthy body.
  • Transmitter and receiver are placed onto the synthetic lengthy body each with a spring loaded system and a viscous coupling fluid between the active surface of the transmitter/receiver, chosen to be inert to the lengthy synthetic body.
  • polyethylene this may be silicon oil based or glycerin.
  • T ransmitter can work for example in the range of 1 -3 MHz, the receiver in the in a lower frequency of 100 to 375 kHz.
  • Dual head transmitters/receivers can be used so that both locations can transmit and receive.
  • the transmitter and receiver are attached to preamplifier, bandwidth-filter and amplifier to be converted into a machine-readable digital signal.
  • a burst pulse signal is introduced via the transmitter which will be conducted by the synthetic lengthy body to the location of the receiver and constitutes the data signal which can be processed in a similar fashion as the X-ray data by the machine learnable model, employing most of the same strategies of data integrity control, data reduction, data processing, classification, and training.
  • the conductivity of sound depends on the crystalline/amorphous nature of the lengthy body and is altered by damage introduced by wear. These changes are non-linear and unobvious to the experimenter, but can be successfully analyzed by a machine learnable model to classify the wear level of a synthetic lengthy body.
  • the present disclosure provides a computer implemented monitoring method for a synthetic lengthy body, comprising
  • acoustic emission data corresponding to the synthetic lengthy body, wherein the acoustic emission data has been obtained from the synthetic lengthy body by applying a load to at least part of the synthetic lengthy body which exceeds the historical load of said lengthy body and sensing the emitted sound in an ultrasonic transducer with an amplifier, the acoustic emission data indicating a time series of voltages,
  • the monitoring device is based on a machine learnable model with input data from acoustics emission.
  • the synthetic lengthy body is clamped along its length over a certain distance (depending on the diameter of the lengthy body) for example over distance of 2 m.
  • One or multiple ultrasonic transducers are placed onto the synthetic lengthy body each with a spring loaded system and a viscous coupling fluid between the active surface of the transmitter/receiver, chosen to be inert to the lengthy synthetic body.
  • a spring loaded system and a viscous coupling fluid between the active surface of the transmitter/receiver, chosen to be inert to the lengthy synthetic body.
  • a viscous coupling fluid between the active surface of the transmitter/receiver, chosen to be inert to the lengthy synthetic body.
  • polyethylene this may be silicon oil based or glycerin.
  • the present disclosure provides a computer implemented monitoring method for a synthetic lengthy body, comprising
  • acoustics-ultrasonic data corresponding to the synthetic lengthy body, wherein the acoustics-ultrasonic data has been obtained from the synthetic lengthy body by coupling a sound wave to at least part of the synthetic lengthy body and sensing the conducted sound wave with a transducer with an amplifier, the acoustics-ultrasonic data indicating a time series of voltages,
  • Integrating multiple information sources may be done by training a model to accept the multiple information sources as multiple inputs, or by training models separately for each information source, e.g., each sensor modality and to combine their model outputs.
  • confidence values may be obtained by the multiple models; in an embodiment, an aggregated level of wear indication may be derived from multiple levels of wear of the multiple models and possibly the confidence values.
  • a model may be trained on use-data, e.g., number of load cycles, load weights, etc., to produce a level of wear indicator and possibly a confidence value. This model output can also be combined with the sensor derived estimations.
  • a further advantage is that internal defects may be detected that are not visible from the outside, e.g., internal melt, internal abrasion, and intra-filament wear.
  • the level of wear may be expressed as predicted remaining lifetime.
  • the testing may be nondestructive. Experiments performed on embodiments were able to classify healthy and worn out UHMWPE ropes with 100 % or near 100%, both with higher and with lower X-ray energy levels.
  • the X-ray data is obtained by directing X-ray radiation tangentially to the synthetic lengthy body, possibly from multiple angles or under lengthy body rotation.
  • An advantage of this approach is that thicker lengthy bodies, that is lengthy bodies having a larger equivalent diameter, and/or lower energy levels may be used to obtain the diffraction data.
  • a rotatable X-ray diffractor may be used; the rotatable X-ray diffractor comprising an X-ray source and a detector connected to a goniometer allowing the source and detector to be rotated around the rope in a controlled way.
  • This has the advantage that the rope can remain stationary. Rotating the rope in the field can be an undesired operation, which can be avoided by rotating the X-ray diffractor.
  • measuring from multiple angles may advantageously be combined with measuring X-ray diffraction data in a tangential fashion, e.g., from a side of the rope.
  • tangential X-ray diffraction data may be obtained from multiples sides, e.g., from a left and right side of the rope, or from a left, bottom, right, and up-side. Measuring from multiple angles provides additional information about the rope; This can reduce the uncertainty, and is especially advantageous in cases with non-uniform wear.
  • the model such as metal or filament synthetic material (e.g., aramid if the rope is made using UHMWPE filaments). If left unattended, the model may produce a level of wear value for such data which may be completely unreliable.
  • a recognition algorithm is applied on the obtained sensor data, e.g., to discard the obtained sensor data if it does not correspond to the expected synthetic lengthy body, e.g., to PE material.
  • the machine learnable model is trained for multiple different types of rope, and/or for different applications.
  • data indicating the type of rope is provided as an input to the machine learnable model.
  • additional information to the machine learning algorithm may include dimensions of the rope, e.g., rope diameter, rope type, e.g., rope braid type. This has the advantage that training material from different types of ropes can be better combined to train a single model.
  • the model may be able to differentiate the different types of ropes from the diffraction data itself, learning will proceed faster and/or with more accuracy if the information is provided to the model.
  • a monitoring device may be installed in a load lifting device, e.g., a crane.
  • a load lifting device such as a crane
  • a typical embodiment of a load lifting device, such as a crane may comprise a sheave and a synthetic lengthy body, such as a rope, arranged for mutual cooperation.
  • An aspect of the invention further concerns a monitoring device for a synthetic lengthy body.
  • An aspect of the invention concerns a training method, or training system, for training the machine learnable model used in a monitoring method or device.
  • additional information for the model comprises information on the original load rating of rope/crane. From X-rays the model may determine, say, by what percentage loading capacity is decreased. Such estimates may also be computed from the output of the model, instead of having the model perform the computation. For example, a decreased rating may be provided, say as a percentage of the full rating, or as a new rating.
  • the monitoring device and training system may be implemented in a computer.
  • An embodiment of the method may be implemented on a computer as a computer implemented method, or in dedicated hardware, or in a combination of both.
  • Executable code for an embodiment of the method may be stored on a computer program product.
  • Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc.
  • the computer program product comprises non-transitory program code stored on a computer readable medium for performing an embodiment of the method when said program product is executed on a computer.
  • the computer program comprises computer program code adapted to perform all or part of the steps of an embodiment of the method when the computer program is run on a computer.
  • the computer program is embodied on a computer readable medium.
  • Fig. 1 schematically shows an example of an embodiment of a device to determine tensile properties of a UHMWPE filament
  • Fig. 2 schematically shows an example of an embodiment of a synthetic lengthy body, the synthetic length body being a synthetic drive chain,
  • Fig. 3a schematically shows an example of an embodiment of a monitoring device for a synthetic lengthy body
  • Fig. 3b schematically shows an example of an embodiment of a monitoring device for a synthetic lengthy body
  • Fig. 3c schematically shows an example of an embodiment of training system for a synthetic lengthy body monitoring system
  • Fig. 3d schematically shows an example of an embodiment of a monitoring device for a synthetic lengthy body
  • Fig. 3e schematically shows an example of an embodiment of a monitoring method for a synthetic lengthy body monitoring system
  • Fig. 3f schematically shows an example of an embodiment of a training method for a synthetic lengthy body monitoring system
  • Fig. 4 schematically shows an example of an embodiment of a load lifting device
  • Fig. 5a schematically shows an example of an embodiment of a monitoring system for a synthetic lengthy body, in a side view
  • Fig. 5b schematically shows an example of an embodiment of a monitoring system for a synthetic lengthy body, in a top view
  • Fig. 5c schematically shows an example of a detail of an embodiment of a monitoring system for a synthetic lengthy body, in a top view
  • Fig. 5d schematically shows an example of a detail of an embodiment of a monitoring system for a synthetic lengthy body, in a top view
  • Fig. 6a schematically shows an embodiment of a synthetic rope
  • Fig. 6b schematically shows directing an X-ray beam to the rim of a synthetic lengthy body
  • Fig. 7 schematically shows an example of an embodiment of lengthy body
  • Fig. 8 schematically shows an example of an embodiment of lengthy body.
  • Fig. 9a schematically shows a computer readable medium having a writable part comprising a computer program according to an embodiment
  • Fig.9b schematically shows a representation of a processor system according to an embodiment
  • Fig. 10a shows an experimental arrangement to apply wear to a rope
  • Fig. 10b shows a disassembling of a rope
  • Fig. 10c shows a fraction of X-ray transmitted through UHMWPE against X- ray energy in keV and thickness of the synthetic material in cm, the contour lines 0.250, 0.500 and 0.750 indicate 25%, 50% and 75% X-ray transmission, respectively.
  • the scale bar on the right indicates X-ray transmission.
  • Fig. 10d shows mean 1 D WAXS curves
  • Fig. 10e shows an example of a high energy WAXS image of a synthetic rope
  • Fig. 10f shows mean 1 D WAXS curves
  • Fig. 10g shows linear discriminant coefficients
  • Embodiments relate to the monitoring of synthetic lengthy bodies.
  • synthetic lengthy bodies include without limitation a rope, a belt, a round sling, a splice, and synthetic chain amongst others.
  • Lengthy bodies may be especially adapted to be used as load-bearing element in many applications such as lifting ropes.
  • the term lengthy body includes but is not limited to a strand, a cable, a cord, a rope, a belt, a sling, a ribbon, a strip, a hose, and a tube.
  • Synthetic lengthy bodies herein comprise high strength synthetic filaments.
  • fiber an elongated body, the length dimension of which is much greater than the transverse dimensions of width and thickness.
  • the term fiber herein includes a filament, such filament may have a regular or irregular crosssection.
  • a filament is an elongated body, the length dimension of which is much greater than the transverse dimensions of width and thickness.
  • Fibers may have continuous lengths, known in the art as filaments or continuous filaments, or discontinuous lengths, known in the art as staple fibers.
  • a yarn for the purpose of the invention is an elongated body comprising at least two individual filaments. The filaments in the yarn may be twisted or untwisted, preferably the filaments of a yarn are untwisted.
  • the length dimension being much greater than the transverse dimensions of width and thickness.
  • said length dimension is at least 10 times, more preferably at least 20 times even more preferably at least 50 times and most preferably at least 100 times greater than the width or thickness dimension whichever is larger.
  • a lengthy body herein is herein understood an elongated body, the length dimension of which is much greater than the transverse dimensions of width and thickness or diameter.
  • Preferably said length dimension is at least 10 times, more preferably at least 20 times even more preferably at least 50 times and most preferably at least 100 times greater than the width or thickness dimension whichever is larger.
  • transverse dimensions of a lengthy body are herein also referred to as width and thickness of the lengthy body and referred to as width and thickness of the rope.
  • Tz and Ty are substantially the same and are typically referred to as the diameter of the rope.
  • Such rope would herein have a thickness of Tz.
  • Rope thickness herein refers to rope diameter.
  • Typical applications of ropes and belts involve applications in which repeated bending occurs, amongst which bend-over-sheave applications. During such applications, the rope is frequently pulled over drums, bitts, pulleys, sheaves, etc., amongst others, resulting in rubbing and bending of the rope. When exposed to such frequent bending or flexing, a rope may fail due to rope and filament damage. Such fatigue failure is often referred to as bend fatigue or flex fatigue.
  • Typical applications of chains include storing, securing, such as securing a roll on/off dumpster to a dumpster hauling truck or freight to commercial trucks, flat bed trailers, lashing and tie down for handling and transporting cargo, in lifting and hoisting, logging, hauling and rigging, propulsion and driving, mooring, cargo-hold of an aircraft or naval ship and the like.
  • a synthetic chain herein typically comprises a plurality of interconnected chain links wherein at least a part of the links comprises high strength synthetic filaments.
  • a synthetic chain may be a chain comprising a plurality of interconnected chain links wherein each link comprises high strength synthetic filaments, such as UHMWPE filaments.
  • a synthetic chain is typically suitable to moor or anchor boats, to lash cargo in road, rail, water and air transportation and suitable for conveying, hoisting, suspending and lifting applications.
  • a rope in the context of the present invention is an elongated body having a length much larger than its lateral dimensions of, for example, width and thickness or diameter.
  • the rope to be used in accordance with the invention may have a cross-section which is circular, rounded or polygonal or combination thereof.
  • diameter of the rope is herein understood the largest distance between two opposite locations on the periphery of a cross-section of the rope.
  • the rope may have a cross-section that is about circular or round, but also an oblong cross-section, meaning that the cross-section of a tensioned rope shows a flattened, oval, or even an almost rectangular form.
  • Such oblong cross-section preferably has an aspect ratio, e.g., the ratio of the larger to the smaller diameter (or width to thickness ratio), in the range of from 1.2 to 4.0, preferably in the range of 1.2 to 2.0.
  • a rope in the context of the present invention may include a belt having a length much larger than its transversal dimensions of e.g., width and thickness.
  • An example of a belt includes a woven strip of yarns, a flexible band or strap and a loop of flexible material which may be used to link two or more deflection components such as sheaves, most often parallel arranged deflection components.
  • the rope in the context of the present invention may include multiple parallel ropes, having at least two interconnected ropes.
  • the cross-section of a tensioned rope herein is the cross section of the rope under a load of 300 MPa.
  • the ropes with oblong cross-section it is more accurate to define its size as a round rope with an equivalent diameter; that is the diameter of a circular rope of same mass per length as the non-round rope.
  • the rope may have an equivalent diameter that varies between wide limits, for example, depending upon the operation conditions and size of the entity in which it is used, such as a crane or an airborne wind energy system.
  • the diameter of a rope in general, however, is an uncertain parameter for measuring its size, because of irregular boundaries of ropes defined by the strands.
  • a more concise size parameter is the linear density of a rope, also called titer or linear weight; which is its mass per unit length.
  • the rope is a rope having an equivalent diameter in the range from 5 mm to 20 mm.
  • the rope may have an equivalent diameter in the range from 5 mm to 50 mm, for example, for use in an Airborne Wind Energy system.
  • the rope is a heavy-duty rope having an equivalent diameter of at least 20 mm, more preferably at least 30 mm, 40 mm, 50 mm, or even at least 60 mm.
  • the rope typically has an equivalent diameter of up to about 350 mm, in an aspect of up to about 300 mm, in an aspect of up to about 250 mm.
  • a thicker rope or thick rope herein is typically referring to a UHMWPE rope having an equivalent rope diameter in the range from 15 mm to 350 mm.
  • a thinner rope or thin rope herein is referring to a UHMWPE rope having an equivalent rope diameter in the range from 1 mm to 15 mm.
  • the amount of synthetic material the X-ray radiation has to travel through and the photon energy of the X-ray radiation source are adjusted to each-other such that the signal-to-noise level is sufficient to allow for analyzing the diffractive X-ray data, and generating a value quantifying a level of wear in the part of the lengthy body. For example, if X-ray diffraction data is desired to be obtained from a thicker rope it may be done via directing the X-ray radiation tangentially to the rope or by using a higher X- ray energy if applicable.
  • a suitable way to select a location on the lengthy body to aim the X-ray radiation beam at, also referred to herein as irradiation zone, is to measure the amount of X-ray transmission through the lengthy body when irradiated at said location.
  • a tangential path is preferred.
  • a synthetic lengthy body such as a rope
  • a Tungsten (W) X-ray source results in ⁇ 75% transmission of the X-ray radiation through the rope
  • tangential irradiation is preferred.
  • the photon energy of W is 59.3 kV
  • W 59.3 kV this would be equivalent to ca. 15 mm UHMWPE, as can be deduced from Figure 10c. Therefore, if a rope is made using UHMWPE filaments and a W X-ray source is used, the skilled person may prefer to apply tangential irradiation if the diameter of such rope is 15 mm or more. The skilled person may then for example direct the radiation to more towards the rim of the rope, such that the primary beam, i.e., the portion of the radiation that is not diffracted, has to travel through ca 15 mm of UHMWPE rope material.
  • a different X-ray source is used (anode X-ray material) this may be applied analogously: using the 75% transmission provides a distinction between a thick and a thin rope, that is between tangentially and through rope irradiation.
  • measuring time is not limiting factor the skilled person may decide to measure longer at a lower transmission threshold e.g., 1.5x longer using a 50% transmission of the X-ray radiation threshold.
  • a higher transmission is preferred, but the transmission threshold can be adjusted as long as multiple scattering does not become predominant in the X- ray diffraction data. In cases where the transmission threshold has to be lowered to levels where multiple scattering becomes predominant, it is advisable to acquire the training data at similar conditions.
  • a rope made from aramid filaments would be considered a thick rope with respect to a Tungsten (W) X-ray source herein if it would result in ⁇ 75% transmission of the X-ray radiation through the rope.
  • W Tungsten
  • this would be equivalent to ca. 11 mm aramid.
  • this a translated to a rope made from aramid filaments having diameter of 11 mm. So, if a rope is made using aramid filaments and a W X-ray source is used, the skilled person may prefer to apply tangential radiation if the diameter of such rope is 11 mm or more.
  • a synthetic lengthy body such as a rope or a belt ora chain, herein comprises high strength synthetic filaments.
  • These synthetic filaments have a filament tenacity of at least 1.0 N/tex, preferably of at least 1 .2 N/tex, more preferably at least 1 .5 N/tex, even more preferably at least 2.0 N/tex, yet more preferably at least 2.2 N/tex and most preferably at least 2.5 N/tex.
  • the high strength filaments are UHMWPE filaments
  • said UHMWPE filaments preferably have a filament tenacity of at least 1 .8 N/tex, more preferably of at least 2.5 N/tex, even more preferably at least 3.0 N/tex and most preferably at least 3.5 N/tex.
  • the filament tenacity of the UHMWPE filaments is at most 4.5, at most 5.0, at most 5.5, or at most 7 N/tex; for example, in an embodiment, the filament tenacity is from 1 .5 N/tex up to 7 N/tex, or from 2.0 N/tex up to 5.5 N/tex.
  • the high strength filaments have a filament tensile modulus of at least 30 N/tex, more preferably of at least 50 N/tex, most preferably of at least 60 N/tex.
  • the UHMWPE filaments have a filament tensile modulus of at least 50 N/tex, more preferably of at least 80 N/tex, most preferably of at least 100 N/tex.
  • the filament tenacity and tensile modulus may be determined using the method Tensile properties of UHMWPE filaments as described herein in an analogous manner.
  • the synthetic lengthy body comprises synthetic UHMWPE filaments having a filament tenacity of at least 1 .5 N/tex.
  • the fraction of material of the synthetic filaments in the synthetic lengthy body in crystalline phase X c [%] is at least 60% as defined by (1 a).
  • the fraction of material in the crystalline phase Xc[%] is at least 80% as defined by (1a).
  • the fraction of material in the crystalline phase X c [%] is at least 90% as defined by (1a).
  • the UHMWPE synthetic filaments in the synthetic lengthy body have an X c [%] of at least 60% as defined by (1 a) and a filament tenacity of at least 1 .5 N/tex.
  • the UHMWPE synthetic filaments in the synthetic lengthy body have an Xc[%] of at least 80% as defined by (1 a) and a filament tenacity of at least 2.0 N/tex.
  • the generating a value quantifying a level of wear includes analyzing a change in the crystal structure, preferably a change in the amount of monoclinic crystal phase, more preferably an increase of the amount of monoclinic crystal phase.
  • the synthetic lengthy body comprises synthetic UHMWPE filaments having a filament tenacity of at least 1.5 N/tex, and generating a value quantifying a level of wear includes analyzing a change in the crystal structure, preferably a change in the amount of monoclinic crystal phase, more preferably an increase of the amount of monoclinic crystal phase.
  • Ropes comprising the high strength filaments may provide high strength. Therefore, the embodiments of the present invention preferably include a rope wherein the rope has a tenacity of at least 0.50 N/tex, preferably the rope has a tenacity of at least 0.60 N/tex, more preferably of at least 0.70 N/tex, even more preferably 0.80 N/tex and most preferably at least 1 .00 N/tex.
  • the term rope system herein includes tether systems.
  • the strength member has a tenacity of at least 0.9 N/tex, preferably at least 1.1 N/tex, more preferably at least 1 .3 N/tex and most preferably at least 1.5 N/tex. Break force may be determined by means of tensile testing (According to, e.g., ISO 2307).
  • the ropes have high tenacity and high diameters.
  • the combination of these features provides ropes, including tethers, with a break strength, also called minimum break load (MBL) of at least 10 kN, more preferably of at least 50 kN and most preferably of at least 100 kN.
  • MBL minimum break load
  • the MBL may be obtained by testing according to ISO 2307, whereby the tenacity of the rope is calculated by dividing said MBL by the titer of the rope.
  • the high strength synthetic filaments are filaments manufactured from and hence comprising a polymer chosen from the group consisting of polyamides and polyaramides, e.g., poly(p-phenylene terephthalamide) (known as Kevlar®); polytetrafluoroethylene) (PTFE); poly ⁇ 2,6-diimidazo-[4,5b-4’,5’e]pyridinylene-1 ,4(2,5- dihydroxy)phenylene ⁇ (known as M5); poly(p-phenylene-2, 6-benzobisoxazole) (PBO) (known as Zylon®); liquid crystal polymers (LCP); poly(hexamethyleneadipamide) (known as nylon 6,6), poly(4-aminobutyric acid) (known as nylon 6); polyesters, e.g., polyethylene terephthalate), poly(butylene terephthalate), and poly(1 ,4 cyclohexylidene dimethylene terephthalate); polyvinyl alcohol,
  • the preferred synthetic filaments are selected from polyaramide filaments and high or ultra-high molecular weight polyethylene (HMWPE or UHMWPE) filaments.
  • HMWPE filaments are melt spun and the UHMWPE filaments are gel spun, e.g., sold in the form of yarns manufactured by DSM Performance Materials, NL (known as Dyneema®).
  • the synthetic filaments are e-PTFE filaments (known as Omnibend®).
  • Liquid crystal polymer (LCP) filaments are known as Vectran®.
  • the synthetic filaments are polyolefin filaments having a filament tenacity of at least 1.5 N/tex, preferably polyethylene, and/or polypropylene filaments having a filament tenacity of at least 1 .5 N/tex.
  • the synthetic filaments are ultra-high molecular weight polyethylene (UHMWPE) filaments, more preferably gel spun UHMWPE filaments.
  • UHMWPE ultra-high molecular weight polyethylene
  • at least 50 wt%, more preferably at least 80 wt% and even more preferably at least 90 wt% and most preferably all of the high strength synthetic filaments present in the synthetic lengthy body are UHMWPE filaments.
  • the synthetic length body is a rope comprising ultra-high molecular weight polyethylene (UHMWPE) filaments, more preferably gel spun UHMWPE filaments.
  • UHMWPE ultra-high molecular weight polyethylene
  • at least 50 wt%, more preferably at least 80 wt% and even more preferably at least 90 wt% and most preferably all of the high strength synthetic filaments present in the rope are UHMWPE filaments.
  • the synthetic lengthy body comprises multiple synthetic polyolefin filaments having a filament tenacity of at least 1.5 N/tex, preferably polyethylene, and/or polypropylene filaments having a filament tenacity of at least 1 .5 N/tex.
  • the synthetic lengthy body comprises synthetic UHMWPE filaments having a filament tenacity of at least 1 .5 N/tex.
  • the tenacity of polypropylene filaments is typically at most 3.0 or 2.0 N/tex. In embodiments, the polypropylene filaments have a tenacity in the range from 1 .5 N/Tex to 2.0 N/Tex.
  • the polypropylene filament tenacity may be determined using the method Tensile properties of UHMWPE filaments as described herein in an analogous manner.
  • the rope comprises ultra- high molecular weight polyethylene (UHMWPE) filaments, more preferably gel spun UHMWPE filaments.
  • UHMWPE ultra- high molecular weight polyethylene
  • at least 50 wt%, more preferably at least 80 wt% and even more preferably at least 90 wt% and most preferably all of the high strength synthetic filaments present in the rope are UHMWPE filaments.
  • the rope is used in a tethered system, said rope comprising load carrying primary strands, said strands comprising high strength yarns, wherein these yarns comprise ultra-high molecular weight polyethylene filaments.
  • at least 50 wt%, more preferably at least 80 wt% and even more preferably at least 90 wt% and most preferably all of the filaments present in the yarns are UHMWPE filaments.
  • the UHMWPE present in the UHMWPE filaments has an intrinsic viscosity (IV) of at least 3 dL/g, more preferably at least 4 dL/g, most preferably at least 5 dL/g.
  • IV is at most 40 dL/g, more preferably at most 30 dL/g, more preferably at most 25 dL/g.
  • the IV may be determined according to ASTM D1601 (2004) at 135°C in decalin, the dissolution time being 16 hours, with BHT (Butylated Hydroxy Toluene) as anti-oxidant in an amount of 2 g/l solution, by extrapolating the viscosity as measured at different concentrations to zero concentration.
  • the UHMWPE comprises short chain branches (SCB) which originate from a co-monomer present in the UHMWPE wherein the co-monomer is preferably selected from the group consisting of alpha-olefins with at least 3 carbon atoms, cyclic olefins having 5 to 20 carbon atoms and linear, branched or cyclic dienes having 4 to 20 carbon atoms.
  • SLB short chain branches
  • An alpha-olefin refers to an olefin with terminal unsaturation having 3 or more carbon atoms, preferably from 3 to 20 carbon atoms.
  • Preferred alpha- olefins include linear mono-olefins such as propylene, butene-1 , pentene-1 , hexene-1 , heptene-1 , octene-1 and decene-1 ; branched mono-olefins such as 3-methyl butene-1 , 3-methyl pentene-1 and 4-methyl pentene-1 ; vinyl cyclohexane, and the like.
  • Alphaolefins may be used alone, or in a combination of two or more.
  • the alpha-olefin has between 3 and 12 carbon atoms. Even more preferably the alpha-olefin is selected from the group consisting of propene, butene-1 , hexene-1 , octene-1. Most preferably propene, butene-1 , hexene-1 are present as co-monomer in the UHMWPE. The applicant found that these alphaolefins may readily copolymerize and may show optimized strongest effect on creep lifetime properties.
  • the UHMWPE comprises at least 0.3 short chain branches per thousand total carbon atoms (SCB/1000TC), more preferably at least 0.4 SCB/1000TC and most preferably at least 0.5 SCB/1000TC.
  • the co-monomer content of the UHMWPE is not particularly limited but for production stability reasons may be such to result in less than 50 SCB/1000TC, preferably less than 25 SCB/1000TC.
  • short chain branches in the present application are understood branches that may originate from a copolymerized co-monomer but also other way like, for example, short chain branches introduced by the catalyst via irregular ethylene incorporation.
  • the UHMWPE of the yarn comprises SCB being C1-C20-hydrocarbyl groups, preferably the C1-C20-hydrocarbyl group is selected from the group consisting of methyl, ethyl, propyl, butyl, pentyl, hexyl, octyl and cyclohexyl, isomers thereof and mixtures thereof as short chain branches.
  • short chain branches are distinguished from long chain branches (LCB) that are herein defined as branches containing more than 20 carbon atoms but are often of substantially higher lengths reaching the dimensions of polymer chains themselves and resulting in a branched polymer architecture.
  • Polymers having substantially no LCB are commonly referred to as linear polymers.
  • the UHMWPE is a linear polyethylene with less than 1 long chain branch (LCB) per 1000 total carbon atoms, and preferably less than 1 LCB per 5000 total carbon atoms.
  • the gel-spun filament comprises an ultra-high molecular weight polyethylene (UHMWPE), wherein the UHMWPE has an intrinsic viscosity (IV) of at least 4 dL/g and comprises at least 0.3 short chain branches per thousand total carbon atoms.
  • UHMWPE ultra-high molecular weight polyethylene
  • IV intrinsic viscosity
  • the rope may be of various constructions, including laid, braided, parallel, and wire rope-like constructed ropes.
  • the number of strands in the rope may also vary widely, but is generally at least 3 and preferably at most 16, to arrive at a combination of good performance and ease of manufacture.
  • the rope is a braided rope, a laid rope, a parallel strand rope, a soutache braided rope or a parallel yarn rope.
  • the rope is a braided rope.
  • the rope is a soutache braided rope.
  • the rope is of a braided construction, to provide a robust and torque-balanced rope that retains its coherency during use.
  • braid types There is a variety of braid types known, each generally distinguished by the method that forms the rope. Suitable constructions include soutache braids, tubular braids, and flat braids.
  • the number of strands in a braided rope is preferably at least 3. There is no upper limit to the number of strands, although in practice ropes will generally have no more than 32 strands. Particularly suitable are ropes of an 8- or 12-strand braided construction. Such ropes provide a favorable combination of tenacity and resistance to bend fatigue, and can be made economically on relatively simple machines.
  • IV the Intrinsic Viscosity for UHMWPE is determined according to ASTM D1601- 99(2004) at 135°C in decalin, with a dissolution time of 16 hours, with BHT(Butylated Hydroxy Toluene) as anti-oxidant in an amount of 2 g/l solution. IV is obtained by extrapolating the viscosity as measured at different concentrations to zero concentration.
  • dtex yarns' titer (dtex) is measured by weighing 100 meters of yarn. The dtex of the yarn is calculated by dividing the weight in milligrams by 10.
  • Clamp slippage during filament tensile testing, preventing filament fracture, is eliminated by adaption of the Favimat clamps of the Favimat according to figure 1.
  • the upper clamp 121 is attached to the load cell (not shown).
  • the lower clamp 122 moves in downward direction (D) with selected tensile testing speed during the tensile test.
  • the filament (125) to be tested, at each of the two clamps, is clamped between two jaw faces 123 (4x4x2 mm) made from Plexiglass® and wrapped three times over ceramic pins 124. Prior to tensile testing, the linear density of the filament length between the ceramic pins is determined vibroscopically.
  • Determination of filament linear density is carried out at a filament gauge length (F) of 50 mm (see figure 1), at a pretension of 2.50 cN/tex (using the expected filament linear density calculated from yarn linear density and number of filaments). Subsequently, the tensile test is performed at a test speed of the lower clamp of 25 mm/min with a pretension of 0.50 cN/tex, and the filament tenacity is calculated from the measured force at break and the vibroscopically determined filament linear density. The elongational strain is determined by using the whole filament length between the upper and lower plexiglass jaw faces at the defined pretension of 0.50 cN/tex.
  • EAB (measured') the measured elonoation at break (%)
  • E 5 elongational strain at a stress of 5 cN/dtex (%)
  • CM(5:10) Chord Modulus between 5 and 10 cN/dtex (N/tex).
  • Tensile properties of UHMWPE yarns tensile strength (or tenacity) and tensile modulus (or modulus) of a yarn are defined and determined on multifilament yarns as specified in ASTM D885M (1995), using a nominal gauge length of the yarn of 500 mm, a crosshead speed of 50 %/min and Instron 2714 clamps, of type “Fibre Grip D5618C”. On the basis of the measured stress-strain curve the modulus is determined as the gradient between 0.3 and 1 % strain using a pretension of 0.2 cN/tex. For calculation of the modulus and strength, the tensile forces measured are divided by the titre, as determined above; values in GPa are calculated assuming a density of 0.97 g/cm 3 for the UHMWPE.
  • Short chain branches per 1000 total carbon is determined by NMR techniques and IR methods calibrated thereon.
  • the amount of methyl, ethyl or butyl short side chains are identical to the amounts of methyl side groups per thousand carbon atoms contained by the UHMWPE as determined by proton 1 H liquid-NMR, hereafter for simplicity NMR, as follows:
  • UHMWPE 800 mg 1 ,1',2,2'-tetracholoroethane-d2 (TCE) solution containing 0.04 mg 2,6-di-tert-butyl-paracresol (DBPC) per gram TCE.
  • TCE 800 mg 1 ,1',2,2'-tetracholoroethane-d2
  • DBPC 2,6-di-tert-butyl-paracresol
  • the UHMWPE solution is placed in a standard 5 mm NMR tube which is then heated in an oven at a temperature between 140° - 150°C while agitating until the UHMWPE is dissolved.
  • the NMR spectrum is recorded at 130°C, e.g., with a high field 400 MHz NMR spectrometer using an 5 mm inverse probe head and set up as follows: a sample spin rate of between 10 - 15 Hz, the observed nucleus -1 H, the lock nucleus - 2H, a pulse angle of 90°, a relaxation delay of 30 sec, the number of scans is set to 1000, a sweep width of 20 ppm, a digital resolution for the NMR spectrum of lower than 0.5, a total number of points in the acquired spectrum of 64k and a line broadening of 0.3 Hz.
  • the two peaks (doublet) of about equal intensity are used to determine the amount of methyl side groups are the highest in the ppm range between 0.8 and 0.9 ppm.
  • the first peak should be positioned at about 0.85 ppm and the second at about 0.86 ppm.
  • A2 is the area of the three peaks of the methyl end groups which are the second highest in the ppm range between 0.8 and 0.9 and are located after the second peak of the methyl side groups towards increasing the ppm range and wherein A3 is the area of the peak given by the CH2 groups of the main UHMWPE chain, being the highest peak in the entire spectrum and located in the ppm range of between 1.2 and 1.4.
  • FIG. 2 schematically represents a synthetic drive chain (400) comprising a strap (402), the strap comprising at least one layer containing a woven fabric and comprising holes (401).
  • t is the length of a chain link
  • w is the width of a chain link.
  • the chain may comprise a layered structure, wherein a plurality of layers containing a woven fabric are stacked and preferably attached to each other preferably by sewing, and wherein links are formed into the layered structure by cutting holes (401) along the structure in a preferably periodical fashion.
  • the strap is having a length (Ls) much larger that its transversal dimensions of, e.g., width (w) and thickness (perpendicular to the plane of the drawing).
  • Such straps can be readily made by weaving or knitting a multifilament yarns such as yarns made from high strength synthetic filaments as described herein into any construction known in the art such as a plain and/or a twill weave construction.
  • FIG. 3a schematically shows an example of an embodiment of a monitoring device 100 for a synthetic lengthy body.
  • Monitoring device 100 comprises a communication interface 150, a storage 140 and a processor system 130.
  • communication interface 150 may be configured to receive diffractive X-ray data, e.g., directly or indirectly from an X-ray sensor.
  • Communication interface 150 may be configured to transmit data, e.g., an output of a machine learning model, e.g., a level of wear, or a warning signal, etc.
  • storage 140 may be configured to store X- ray input data, model output data, model parameters, and so on.
  • the processor system 130 may be configured to process diffractive X-ray data and/or to apply the model to it.
  • Processor system 130 may be configured to execute computer instructions stored in storage 140.
  • Device 100 may in addition or instead be configured to train the machine learnable model.
  • Storage 140 may comprise a local storage of device 100, e.g., a local hard drive or memory.
  • Storage 140 may be non-local storage, e.g., cloud storage. In the latter case, storage 140 may be implemented as or comprise a storage interface to the nonlocal storage.
  • Processor system 130 may comprise one or more local microprocessors.
  • Processor system 130 may comprise one or more non-local processors, e.g., implemented in the cloud.
  • Device 100 may be implemented as a system, the parts of which are distributed across different locations.
  • Device 100 may communicate with external storage, input devices, output devices, and/or with one or more sensors over a computer network.
  • the computer network may be an internet, an intranet, a LAN, a WLAN, etc.
  • the computer network may be the Internet. If device 100 is implemented as a system, the computer network may also be used for internal communication.
  • the device may comprise a connection interface which is arranged to communicate within the device or outside of the device as needed.
  • the connection interface may comprise a connector, e.g., a wired connector, e.g., an Ethernet connector, an optical connector, etc., or a wireless connector, e.g., an antenna, e.g., a Wi-Fi, 4G or 5G antenna.
  • Device 100 may comprise the X-ray sensor, or the X-ray sensor may be external to device 100.
  • the execution of device 100 may be implemented in a processor system, e.g., one or more processor circuits, e.g., microprocessors, examples of which are shown herein.
  • Figures 3b-3d show functional units that may be functional units of the processor system.
  • the figures may be used as a blueprint of a possible functional organization of the processor system.
  • Processor circuit(s) are not shown separate from the units in these figures.
  • the functional units shown in these figures may be wholly or partially implemented in computer instructions that are stored at device 100, e.g., in storage 140, e.g., an electronic memory of device 100, and are executable by a microprocessor of device 100.
  • functional units are implemented partially in hardware, e.g., as coprocessors, e.g., mathematical, machine learning, e.g., neural network coprocessors, and partially in software stored and executed on device 100.
  • Parameters of the network and/or training data may be stored locally at device 100 or may be stored in cloud storage.
  • Figure 3b schematically shows an example of an embodiment of a monitoring device 100 for a synthetic lengthy body.
  • Embodiments of a monitoring device corresponding to figure 3b may be implemented on a device such as device 100 of figure 3a; embodiments may be implemented on a suitably configured computer, etc.
  • FIG. 3b schematically shows a synthetic lengthy body 200 for monitoring by monitoring device 100.
  • Monitoring device 100 may be employed during use of synthetic lengthy body 200; for example, monitoring device 100 may be incorporated in a lifting device or the like. This is not necessary, monitoring device 100 may also be used while the lengthy body 200 is currently not in use.
  • monitoring wear of a synthetic lengthy body is important for many types of synthetic lengthy bodies. With increased wear the risk of failure increases — failure which can be unacceptable for many types of load that may be entrusted to the lengthy body.
  • a type of synthetic lengthy body for which monitoring is important is synthetics ropes. Although some examples below refer to ropes it is understood that wear management is also important for other types of synthetic lengthy bodies, such as synthetic chains, synthetics belts or a synthetic drive chains.
  • the source of the wear can vary.
  • wear may be caused by repeated bending over a sheave, or large and/or repeated tensile strain, etc.
  • a particular problematic class of wear is wear which is internal to the synthetic lengthy body.
  • the wear may comprise one or both of internal melt, and/or internal abrasion.
  • Probing internal wear, e.g., internal defects is important: on the one hand, the majority of the material of synthetic lengthy body is internal, that is, inside the synthetic length body, yet visual inspection or other optical methods can only monitor external wear, e.g., defects visible on the outside of the lengthy body.
  • the wear in a synthetic lengthy body may be the accumulation of wear of various sources, e.g., internal melt, and/or, internal abrasion, and/or, repeated bending over a sheave, and/or, tensile strain, intra-filament wear, in particular, intrafilament wear due to bending.
  • various sources e.g., internal melt, and/or, internal abrasion, and/or, repeated bending over a sheave, and/or, tensile strain
  • intra-filament wear in particular, intrafilament wear due to bending.
  • External abrasion resistance is important in many rope applications. Concentrated damage due to rubbing against abrasive surfaces such as sheaves, fairleads, drums, flanges, winches, bollards and distributed damage due to dragging on deck or the ocean floor can both cause significant strength loss. Nevertheless, internal abrasion between filaments, yarns, and strands is one of the principal causes of rope degradation, especially in cyclic tension or bend-over-sheave service. The internal abrasion ultimately leads to intra-filament damage.
  • filament damage accumulation is detected using X-ray, with this technique changes in the crystalline domains are detected such as a change in the number of domains, in the size of the domains, in crystalline shape, distance between crystal planes, and I or in orientation of the crystal.
  • a polyethylene lengthy body e.g., a synthetic lengthy body comprising filaments made from polyethylene, in particular UHMWPE
  • increased wear causes a change in crystalline structure and/or morphology.
  • an aramid lengthy body e.g., a synthetic lengthy body comprising filaments made from aramid
  • the crystalline/amorphous structure and/or morphology in the synthetic lengthy body changes with wear.
  • These internal changes in the synthetic lengthy body cause changes in the measurements that may be obtained with diffractive X-ray sensing.
  • the relationship between level of wear and the particular change in the synthetic lengthy body is not straightforward.
  • the intra-filament alignment in a synthetic lengthy body may also change, e.g., with a decreasing organization; likewise for other filament damage, e.g., broken filaments.
  • Better aligned fibers have better mechanical properties. Alignment can be lost locally, e.g., because of internal abrasion, e.g., because it cuts the fibers. Alignment loss can be derived from the diffractive X-ray data.
  • like crystalline structure changes the relationship between a level of filament alignment and level of wear is not straightforward. Analysis becomes more complex when filament alignment is taken as an indicator of level of wear together with one more of these other indicators, e.g., crystalline changes. Using a machine learning model makes it possible to incorporate these disparate level of wear indicators. For example, intra-filament alignment decreases during internal melts, which may occur in use, and which cause wear.
  • Intra-filament molecular alignment e.g., of PE molecules or PE crystals
  • Intra-filament molecular alignment can be measured from the 2D x-ray data, in particular before radial integration.
  • the x-ray scattering pattern will be more anisotropic the more oriented the filaments are.
  • the radially integrated data may be offered as an additional input of the model.
  • a machine learnable model receives as input both radially integrated X-ray data and azimuthally integrated X-ray data.
  • FIG. 3b Shown in figure 3b is an X-ray sensor 110 and an X-ray source 111.
  • X-ray sensor 110, X-ray source 111 and synthetic lengthy body 200 are shown in figure 3b external to device 100.
  • Device 100 may be configured to obtain diffractive X-ray data 112 from sensor 110, e.g., through a communication interface, e.g., a sensor interface.
  • X-ray sensor 110, X-ray source 111 and synthetic lengthy body 200 may be incorporated in another device, e.g., a lifting device.
  • X-ray sensor 110 and X-ray source 111 may also be part of device 100. The latter is of advantage in an installation where, say, a rope is monitored while in use.
  • source 110 and source 111 may be spacers, etc., for aligning and spacing the synthetic lengthy body.
  • X-ray source 111 is configured to direct X-ray radiation to at least part of the synthetic lengthy body 200.
  • X-ray sensor 110 is configured to sense diffracted X-ray radiation coming from lengthy body 200.
  • the diffractive X-ray data 112 corresponds to the synthetic lengthy body 200 and has been obtained therefrom.
  • the diffractive X-ray data may indicate a diffraction image.
  • the 111 may be configured for obtaining diffractive X-ray data for the lengthy body 200.
  • the diffractive X-ray data may be obtained from a single direction, and/or with a single X-ray wavelength. Multiple directions and/or wavelengths may be used to improve the information obtained from the lengthy body. It is possible to obtain multiple sets of diffractive X-ray data, e.g., multiple X-ray diffraction images, even from the same location in the rope, e.g., with different direction/angle or wavelength. Multiple sets of diffractive X-ray data may be incorporated in the machine learning model.
  • An advantage of using diffractive X-ray is that internal wear indicators such as those mentioned above cause a change in the diffractive X-ray data and thus can be used to determine a level of wear therefrom.
  • the X-ray radiation emitted from source 111 may have various photon energies. Experiments at lower and at higher photon energies have both shown that level of wear can be determined. An advantage of lower photon energies is that the installation can be smaller and more compact. An advantage of higher photon energies is that the data that is obtained may comprise more information and may penetrate through thicker ropes. Moreover, as X-ray absorption in the rope goes down with higher energy it is easier to monitor thicker ropes at higher energy levels. Experiment showed that it was easier to get high reliability using higher photon energies. On the other hand, at lower energy useful predictions of level of wear can be made; as discussed below there are various strategies to increase the signal-to-noise, also for lower energy levels.
  • the photon energy may be from at least 5 keV, for example, the X-ray radiation having a photon energy in a range from 5 keV up to 400 keV.
  • X-ray sources are easily available well over 100 keV, e.g., at 160 keV, 230 keV.
  • an alloy source the energy level may be varied.
  • Useful ranges include, but are not limited to, e.g.,
  • keV from at least 50 keV, e.g., in the range from 50 keV up to 100 keV, e.g., of 88 keV.
  • This higher energy range gives a similar pattern in the diffractive X-ray data.
  • one can penetrate deeper in the material e.g., go through thicker materials.
  • polyethene material does not absorb as much X-ray energy at higher keV levels.
  • An intermediate position between 35 keV and 50 is also possible.
  • Upwards of 100 keV may also be used, e.g., from 100 keV up to 230 keV; or from 100 keV up to 400 keV, etc.
  • synchrotron-based high- energy x-ray diffraction A synchrotron may be used to obtain high energy x-ray radiation.
  • d-spacings crystal lattice plane distances
  • the d-spacing (d) is defined by the following equation: where Q is the magnitude of the scattering vector and 29 is the scattering angle, i.e., the smallest angle between the incident beam and the scattered beam.
  • Q is the magnitude of the scattering vector
  • 29 is the scattering angle, i.e., the smallest angle between the incident beam and the scattered beam.
  • a minimum and maximum d-spacing can be measured, relating to a maximum and minimum measurable scattering angle, respectively.
  • the maximum measurable scattering angle is related to the maximum distance between the two points where the scattered and the incident (unscattered) beams intersect with the detector surface (a technical implementation of a 1 D detector, e.g., line detector, has a surface too) and the distance from the sample to said detector by a simple trigonometric relationship, given that the incident beam is parallel to the normal vector of the detector.
  • the maximum d-spacing is limited by the smallest scattering angle that can be measured, which is limited by the size of the beamstop and the sample-to-detector distance.
  • the minimum d-spacing obtainable is governed again by the sample-to-detector distance and the maximum distance between the intersects of the scattered and unscattered beam with the detector.
  • Values for the detector size and sample-to-detector distance should be chosen so that the relevant crystal lattice plane distances can be measured.
  • the most important d-spacings with respect to this invention lie between 0.35 and 0.50 nm. Since the proposed machine learning algorithm can select the most relevant d-spacing on its own, it may be beneficial to increase the measured range to fall within 0.25 and 0.6nm, more preferably between 0.15 and 0.7 nm and even more preferably within 0.1 and 1 nm.
  • the optimal d-spacing range for any polymer material can be described as follows: obtain the scattering intensity as a function of d-spacing within the range of 0.15 and 0.7nm, which for most polymers used for synthetic lengthy bodies is known in the literature and select highest intensity peak, i.e., the most prominent peak.
  • the lower bound of the desired d-spacing range will be obtained by subtracting the delta of 0.10 nm, the upper bound by adding the delta of 0.10 nm to the d-spacing of the most prominent peak.
  • the resulting d- spacing range to be measured would thus lie between 0.31 and 0.51 nm.
  • the added or subtracted delta is expanded and is 0.15 nm, even more preferably 0.2 nm and most preferably 0.5 nm.
  • the subtracted delta shall not be larger than the d-spacing of the most prominent peak minus 0.05 nm, so that the resulting range is always positively defined.
  • the X-ray source 111 is point focused, e.g., where the X- ray beam from the source has a circular cross-section. This is a typical choice, as recorded data from the point-focused source is more easily interpretable. It is also possible to use different primary beam cross-sections. It is also possible to use a line- focused source, which increases the X-ray flux compared to the point-focused source. Various orientations of the line with respect to the rope are possible, e.g., in an embodiment, and orientation parallel to the synthetic lengthy body is used. Other orientation could also be used though. For example, the line-focused source from a Kratky-camera system may be used.
  • line-focus is not advised for oriented systems, as data becomes convoluted over the line.
  • a line-focused X-ray source should not be applied to a rope, since it is an oriented and non-homogenous system.
  • the data obtained from the line-focused X-ray source would be very hard to interpret.
  • deconvolution is not needed to make the classification. Since the diffractive X-ray data is correlated to wear, the machine learning model can interpret the data using this correlation — ease of human interpretation is not a factor for a machine learning model.
  • An advantage of line-focus is that a higher flux can be more easily obtained, e.g., less time needed to record one set of X-ray data.
  • the X-ray source is line-focused.
  • the X-ray source 111 and sensor 110 may be installed in a load lifting device, e.g., in a crane or the like.
  • Figure 4 schematically shows an example of an embodiment of a load lifting device 800, in this case a crane.
  • Load lifting device 800 comprises a synthetic lengthy body 820, e.g., a rope.
  • Load lifting device 800 is arranged to lift a load 830 with the synthetic lengthy body 820.
  • Installed in load lifting device 800 is a monitoring device 810, e.g., such as monitoring device 100, configured for the synthetic lengthy body.
  • the monitoring device may be distributed over multiple locations, e.g., X-ray source and X-ray sensor may be installed in lifting device 800, while the machine learnable model may be running on a computer located elsewhere. There may be further elements in figure 4 than shown, e.g., sheaves, drums, etc.
  • the advantage of obtaining diffractive X-ray data in lifting device 800 is that the rope does not need to be decommissioned in order to obtain information on the level of wear of the rope.
  • shadowing may occur.
  • about half of the X-ray beam may be shadowed and the nonshadowed part is measured.
  • This installation can be made compact for installing on a crane, e.g., adapting the X-ray optics. Even with shadowing one can still get enough data due to symmetry of the scattering pattern.
  • the data is centrosymmetric, so even with shadowing part of the rope, does not lose information, at most intensity. Accordingly, an embodiment can be used even in an installation where the measuring geometry does not allow one to scan the whole synthetic lengthy body.
  • the shadowed data can be used.
  • Data quality may be improved by modifying the geometry of detection in this case; For example, having two detectors to extend the recording of the data on the side where there is no shadowing.
  • the detector is moved in case of shadowing. This may be done automatically, for example, shadowing may be detected from the X-ray data, from which in turn a detector move signal may be generated to effect a movement of the detector to capture more of the non-shadowed data.
  • a monitoring device such as monitoring device 100 or 810 may be configured for under water use.
  • one or more spacers may be arranged for spacing the lengthy body form the X-ray source and X-ray sensor while the lengthy body is submerged in water during operation of the X-ray source and X-ray sensor.
  • the quality of diffractive X-ray data obtained at a given wavelength increases.
  • excellent data may be obtained for a rope thickness of 2 mm, e.g., measured as the largest diameter of the rope in the direction of the x-ray radiation.
  • Rope samples may be analyzed of very low thickness, e.g., having a diameter as low as 20 micron, such as having a diameter in the range from 20 to 100 micron.
  • Rope thickness need not be a limiting factor in diffractive X-ray monitoring, as one or more of the measures described below or elsewhere herein can be used.
  • thicker ropes can be probed by increasing X-ray energy, e.g., using a high-energy X-ray source, e.g., a synchrotron or any of the other options available in the art.
  • a high-energy X-ray source e.g., a synchrotron or any of the other options available in the art.
  • machine learning turns out to be particular good solution for analyzing the multiple scattering coming from the thicker ropes.
  • the relationship between scattering and wear is hard even for thin ropes, but machine learning can quantify this relationship both for thin and thick ropes, and at low and high energy levels. It was found to be one of the advantages of applying machine learning that the relationship which becomes increasingly hard with increasing rope thickness can still be tackled.
  • multiple measurements may be taken to improve the data.
  • Multiple measurements can be taken, for example, at different positions in the same rope.
  • Multiple measurements can be taken at different settings, e.g., direction or energy level.
  • Multiple measurements can be aggregated or may be offered to as multiple inputs to a single model. Additional inputs to the model may also or instead come from other sensor modalities than diffractive X-ray, and can be for example as simple as diameter of the lengthy body.
  • a further way to deal with thicker ropes in particular, ropes having a diameter, such that the X-ray radiation does not go through the full diameter of the rope, e.g., rope having a diameter of > 3 cm, is to aim the x-ray on a strand in the rope, or to fully or partially disassemble the rope.
  • the latter allows for easier aiming towards a single strand and this to obtain better x-ray data.
  • figure 10b shows the disassembling of a rope.
  • X-ray data may be obtained by directing X-ray radiation to at least part of a strand isolated from the part of the lengthy body.
  • synthetic lengthy bodies e.g., ropes, can be successfully monitored for wear having any diameter.
  • Figures 5a-5d further illustrates one of the techniques to improve monitoring a thicker rope.
  • Figure 5a schematically shows an example of an embodiment of a monitoring system for a synthetic lengthy body, in a side view.
  • Figure 5b schematically shows the same embodiment but in a top view. Shown in figure 5a is an X-ray source 5, a lengthy synthetic body 4, in this case a rope 4, and an X-ray sensor. Instead of a rope, this embodiment could also be implemented with another type of lengthy body, say a synthetic chain.
  • the X-ray source 5 is configured to direct X-ray radiation 1 to at least part of rope 4. As shown, the rope is not disassembled, though it could be.
  • An X-ray sensor 6 is arranged to obtain diffractive X-ray data corresponding to rope 4.
  • Source 5 and sensor 4 are arranged to scan lengthy body 4 to obtain diffraction data, e.g., a diffraction image.
  • Sensor 6 is configured to measure the diffractive X-ray parts 2 and 3.
  • a beam stop 8 is schematically indicated on sensor 6 to block the non-diffractive part of the X-ray radiation.
  • a digital mask may be applied to the measured X-ray data, e.g., to crop the area of the beamstop plus its immediate vicinity or areas where dead pixels are, etc.
  • the scattering angle e.g., the smallest angle between the incident beam and the scattered beam, has been indicated in figure 5a as ‘20’, not to be confused with diffractive X-ray part 2.
  • the diffractive X-ray data is sent to a computer 7 which is configured according to an embodiment.
  • computer 7 may be configured to apply a machine learnable model to the diffractive X-ray data, in some processed form.
  • computer 7 may be configured to generate a warning signal if a level of wear obtained from machine learnable model’s output exceeds a wear threshold.
  • Figures 5a and 5b show a particular orientation of the synthetic lengthy body, but the orientation may be different.
  • Figure 5c schematically shows a first example of a detail of an embodiment of a monitoring system for a synthetic lengthy body, in a top view.
  • the X-ray radiation is directed, e.g., aimed, at the center of lengthy body 4.
  • Figure 5d schematically shows a second example of a detail of an embodiment of a monitoring system for a synthetic lengthy body, in a top view.
  • the X-ray radiation is directed tangentially to the lengthy body.
  • Figure 5d also shows an example of directing the X-ray radiation towards a strand of the lengthy body, in this case, without isolating the strand.
  • the side may have a thickness of about 2 mm, in a range from 1 mm up to 3 mm.
  • the side thickness may be measured as the distance passed through the rope in the direction of a center line of the beam. Note that full control over the side of the synthetic lengthy body through which the X-ray beam passes is possible by precisely focusing the X-ray beam; note that focusing can be done down to the nanometer scale if needed. Focusing may be used to define the illuminated area on the rope. Seeking the shadowing position can be achieved by translation of the rope in reference to the beam. This could also be done by focusing the x-ray beam onto another location, e.g., closer to thicker area.
  • An advantage of obtaining diffractive X-ray data tangentially, e.g., from a side, is that high-quality X-ray data is obtained without having to disassemble the rope.
  • the level of wear in a rope used in the lifting device can be estimated without having to remove the rope from the lifting device.
  • Tangential direction of X-ray radiation may use a point-focused X-ray source, or a line-focused X-ray source.
  • the X-ray may be focused along the rope, e.g., in a slit-format. This reduces measurement time. Analyzing such data with conventional tools is hard, but interpretation can be done with a machine learning model.
  • FIG. 6a schematically shows an embodiment of a synthetic multi-strand rope 500, in this case a braided 12 strand rope (12x1), which is an example of a synthetic lengthy body.
  • the braided strand rope has a braiding pitch I, a length dimension L (depicted along the x-axis) and transverse dimensions Tz and Ty (along the z and y- axis).
  • the transverse dimensions of a rope are herein also referred to as width and thickness of a rope.
  • Tz and Ty are substantially the same and typically referred to a diameter of the rope. Such rope would herein have a thickness of Tz.
  • the braided rope 500 comprises multiple strands, three of which are shown with a reference numeral: strands 501 , 502, and 503.
  • Strands 501 , 502 and 503 have strand direction indicated as a solid line.
  • two strands can be parallel or crossing.
  • Strand 501 and 502 are crossing strands.
  • Strands 501 and 503 are parallel strands.
  • Figure 6a shows a first example of directing an X-ray beam.
  • X-ray beam 520 is directed towards, e.g., focused on, a zone 510 where two strands cross; in this case strands 501 and 502.
  • X-ray beam 520 will be perpendicular to zone 510.
  • the X-ray beam could be pointed at the parallel zone 511.
  • the X-ray beam 520 is pointed at an area of crossing strands, e.g., zone 510 as this is deemed very effective.
  • Figure 6a shows a second example of directing an X-ray beam.
  • the X-ray beam 521 is directed to the edge, e.g., the side of the rope, e.g., at a side zone.
  • the X-ray beam may be directed perpendicularly through the rope.
  • beam and rope may make a straight angle.
  • X-ray beam 521 is pointed perpendicular, e.g., along the z -axis, to the length dimension L of the rope to a point located in de rim 530 of the rope.
  • the X-ray radiation travels through ca 3 mm of rope material.
  • aiming the X-ray radiation to a crossing zone i.e., a zone 510 where two strands cross, is preferred, as it is expected to be the most effective.
  • Figure 6b schematically shows directing an X-ray beam to the rim of a synthetic lengthy body.
  • the figure schematically depicts a cross section of a lengthy body 700, in this case a braided 12 strand rope (12x1).
  • the cross section schematically shown in figure 6b corresponds to the rope schematically shown in figure 6a.
  • the braided rope 700 comprises a strand 701 .
  • An X-ray beam 730 is pointed perpendicular (along the z - axis) to the length dimension of the rope and measured in the rim of the rope.
  • the X-ray radiation travels through ca 3 mm of rope material.
  • the X- ray beam may be directed through 2 mm to 4 mm of rope material.
  • Figure 7 schematically depicts a lengthy body 60, in this case a chain comprising chain links. Two chain links have a reference numeral in figure 7: chain link
  • the X-ray beam (not shown in figure 7) may be pointed at the synthetic material of the chain.
  • the X-ray beam may be directed at zone
  • the beam is pointed at the contact location 62 through which loads are directly transmitted between said chain links.
  • Figure 8 schematically depicts a lengthy body 90, in this case a laid rope comprising strands 91 having an outer surface 92 on the outside of the rope.
  • the X-ray beam 94 may be pointed at rope 90.
  • X-ray beam 94 may be pointed at a strand contact area where two strands contact each other; for example, in an area indicated with zone 93.
  • the X-ray beam may be pointed perpendicular (along the z - axis) to the length dimension L of the rope to a point located in de rim (95) of the rope.
  • ca 3 mm of rope e.g., between 1 mm and 4 mm.
  • the length of rope through which the X-ray beam passes depends on energy in the X-ray beam.
  • Monitoring device 100 comprises a machine learnable model 160 that is provided in the device.
  • device 100 may comprise a storage, e.g., a memory comprising trained parameters of model 160.
  • the machine learnable model may be associated with multiple parameters that determine how an output value is obtained from the input values.
  • the multiple parameters may be adapted in a training process so that the machine learnable model learns to approximate data for which the relationship between sensor data and a level of wear is known.
  • Figure 10g which is further discussed below provides an example of model parameters; the figure shows in the form of a graph the parameters of an LDA model.
  • machine learnable model 160 produces a quantifying value 161 that indicates a level of wear.
  • Many types of machine learnable model may be used.
  • regressive ML models may be used, which learn to approximate a level of wearvalue from the input values.
  • a regressive model may produce a quantifying value that an amount of lifting, e.g., in weight, in time, or in weight and time, lengthy body 200 has yet endured.
  • the machine learnable model may be a classification model, e.g., trained on classifying the input sensor data in a level of wear class.
  • the classification model may be used to predict a level of wear as well.
  • an output that indicates likelihood that a rope is broken may be used as an indication of a level of wear.
  • the quantifying value may be a numerical value that indicates a particular wear class.
  • diffractive X-ray data relates to a level of wear information, e.g., because the crystalline/amorphous structure in a synthetic rope changes with use, which in turn affects the diffractive X-ray data.
  • the relationship between diffractive X-ray data and the condition of the rope is non-linear and non-obvious.
  • the state of the art does not provide quantitative models linking the crystalline morphology to the mechanical properties of the rope. It was found that this correlation can be made explicit in a practical method using a machine learning model.
  • the diffractive X-ray data is processed before inputting it to the machine learnable model.
  • Processing may be relatively minimal, e.g., collecting multiple sensor data, applying a filter, or the like.
  • Such approaches can be used for example, when applying ML models with a high number of parameters, and when using a large amount of training data.
  • An example of such a model is a neural network.
  • An advantage of applying a dimension reduction algorithm is that the number of parameters in the trained model can be reduced. Accordingly, fewer samples are needed fortraining the model. It was found that pre-processing the data can also improve the machine learning. For example, a potentially problematic aspect of scanning using diffractive X-ray data is the large number of sensor values, due to the high dimensionality of the data. An advantage of concentrating the available information into fewer data items may be that the model will better learn to generalize.
  • dimension reduction avoids potential problems with aligning the rope. Tilting the rope will tilt the X-ray data, but by performing the dimension reduction the tilting may be removed.
  • a dimension reduction algorithm is not required. For example, a large parameter model such as neural network may be trained on diffraction data without dimension reduction. This has an advantage since the construction of the rope is also affected by the wear. Construction changes will be better visible in the unreduced diffraction data.
  • dimension reduction is from two to one dimensions.
  • the X-ray source, and sensor may cooperate to produce a 3-dimensional X-ray diffraction tomography which records a spatially-resolved X-ray diffraction image of the synthetic lengthy body.
  • dimension reduction may be from 3 to 2 dimension or from 3 to 1 dimensions.
  • the dimension reduction algorithm comprises azimuthally integrating. This is an example of reducing from 2 dimension to 1 dimension.
  • the diffractive x-ray data may be pre-processed by computing particular values. For example, these may be values that are known how to compute analytically, and are known to be relevant but particular relationship between those values and a level of wear may nevertheless be unknown.
  • the preprocessing may comprise calculating a fraction in the part of the lengthy body in a specific crystalline phase. In that case, the machine learnable model may be applied to calculated values.
  • the evolution of morphology during mechanical wear, as a physical phenomenon is non-linear in nature. For instance, the evolution of crystallite sizes, content of each crystal phase and evolution of the orthorhombic unit cell dimension have been shown to be nonlinear with respect to mechanical wear.
  • pre-processing may comprise calculating a fraction in the part of the lengthy body in monoclinic crystalline phase, orthorhombic crystalline phase, and/or overall crystallinity.
  • the machine learnable model may be applied at least to these calculated fractions.
  • the machine learnable model may receive as input one or more computed values as well as dimensional data.
  • a neural network may receive 1 or 2 dimensional input as well as computed crystalline phase fractions.
  • the machine learnable model may be configured to receive the processed diffractive X-ray data and to generate a value quantifying a level of wear in the part of the lengthy body.
  • the predictive accuracy of the machine learnable models was found to be surprisingly high, given the inherent complexity of the problem. For example, even using low-energy diffractive data combined with a geometry with small d-spacing range, which comprises relatively little information and more noise compared to high-energy diffractive data and a geometry with large d-spacing range a predictive accuracy of over 90% can be obtained (in this experiment, the model classified input data into high versus medium or low wear).
  • Monitoring device 100 may comprise a post-processing device 170.
  • post-processing device 170 may be configured to generate a warning in dependence on the generated value.
  • device 170 may comprise a display or a speaker to display or to sound a warning, or show the value, and so on.
  • Device 170 may be configured to derive other measures from the value generated by the trained model, e.g., to go from level of wear to estimated remaining lifetime, etc.
  • the level of wear predicted by the model may interpolate between known wear classes.
  • the level of wear may correlate with various wear indicators;
  • the level of wear may correlate to and/or be trained on residual break strength, number of equivalent rope cycles.
  • wear may be expressed as the number of equivalent cycles in a controlled wear experiment.
  • the model may be trained to predict the number of wear cycles a rope experienced in training. Even if the actual wear in use is different the resulting prediction may be used as a level of wear indication.
  • the level of wear obtained from the model may be used as a discard criterion.
  • a warning signal may be generated if the level of wear indicated by the value exceeds a wear threshold.
  • the signal may be displayed on a display, or a sound may be generated, etc.
  • the level of wear may indicate at what wear level the rope needs to be discarded. For example, if a level of wear exceeds a threshold, then this may be used as a signal to discard the rope.
  • the level of wear generated by the model may be used in combination with other indicators of wear. For example, a number of cycles or an amount of time the rope has been used may be used a further indicator.
  • the unit in which the level of wear is expressed may be determined by the data on which the model is trained. For example, a model may be trained on data bins with low, medium, and high wear, as a result of which the model may output values low, medium, and high wear. For example, the model may interpolate between low, medium, and high wear.
  • the particular threshold depends on the application, e.g., on the risk that can be taken.
  • an estimated remaining lifetime may be derived from the generated value, e.g., from the level of wear.
  • the estimated remaining lifetime may be expressed in a similar metric as the level of wear. For example, in an estimated number remaining predefined cycles. The actual remaining lifetime will depend on the future rate of degeneration, which might not be known. Remaining cycles may be used as a proxy for remaining lifetime. For example, a look-up table or a function, etc., may translate level of wear into estimated lifetime.
  • a remaining lifetime estimator may be built up using a nearest neighbor algorithm on further training data. For example, a training set may be obtained comprising pairs of level of wear as predicted by the model, and actual remaining lifetime.
  • a further machine learning model may map the former to the latter, e.g., the nearest neighbor algorithm.
  • the monitoring device e.g., the machine learnable model
  • the device is configured to compute a confidence level for the level of wear or, in case of multiple samples, an aggregated level of wear.
  • the device may be configured to compute multiple values, e.g., multiple levels of wear, e.g., from multiple samples of the same rope. The average of those multiple levels of wear may be taken as the aggregated level of wear, and a standard deviation of the multiple levels of wear as a confidence value. Note that wear is not a point phenomenon, but occurs over an extended length. Accordingly, it is expected that multiple values will be similar, so that taking them in to account together increased accuracy.
  • Multiple measurement may be taken at a distance along the rope, but may especially also be taken by rotation of the rope, e.g., measuring X-ray under multiple angles at the same or about the same length along the lengthy body.
  • the latter was found to be especially suited for combining, e.g., averaging, multiple levels of wear indications.
  • a user can configure the length along the body over averaging is done, e.g., is permissible.
  • a suitable range may depend on the application of the rope, so that in some application wear may vary stronger over shorter rope lengths than in other application.
  • a map may be generated indicating that wear in different parts of the rope, e.g., to enable the skilled person when taking a decision on partial rope replacement.
  • a confidence value may indicate how similar the current input data is to input data seen during training. If the input data conforms to a data distribution known from training, then it is likely that a good value will be computed for it. Known machine learning methods are available to estimate how close a new input is to data seen during training. If the input data does not conform to a data distribution known from training, then the model will be able to compute a value for it, but it is unlikely that it is realistic.
  • a warning signal is generated if the current input data does not conform sufficiently to the training data, e.g., within a predetermined bound. The system may be configured to refuse to output an unrealistic value in such a case.
  • a confidence value may be used as an additional discard criterion. For example, if confidence is high and level of wear is high, e.g., over some thresholds, then the rope may be discarded. But if confidence is low, other indicators may be used, e.g., known history of the rope, e.g., number of previous load cycles etc.
  • a confidence value is to use it as an indicator to obtain additional data. For example, if confidence is low then additional diffraction X-ray data may be obtained and analyzed. For example, the device may be configured to generate a warning signal if the confidence level is below a confidence threshold and/or obtaining further X-ray data and recomputing the level of wear or aggregated level of wear. Additional data may be obtained from a different part of the synthetic lengthy body, but may also refer to another rotated position or different tangential position if the measurement was not centered on the rope, e.g., on the other side of the rope. If the measurement was centered on the rope, then an additional measurement may measure tangentially.
  • Predictions can be aggregated, e.g., by taking the worst level of wear from multiple levels of wear indications. Multiple level of wear values may be combined, say, by discarding the best and worst value, and averaging the rest. If data is aggregated, the model may generate a level of wear and a confidence value for each of the multiple inputs, e.g., multiple diffractive X-ray data. The multiple levels of wear may be combined into one aggregated level of wear, while the multiple confidence values may be combined into one aggregated confidence. For example, the aggregated level of wear may be a weighted average, with the weights derived from the confidences, e.g., equal to them.
  • the following formula is used for the confidences.
  • C represents the class, e.g., in this particular example these may be the classes NBZ, SBZ, DBZ;
  • p c l represents the normalized joint probability for class C and measurement number (i);
  • p k l represents a probability estimate for a measurement number (i) and a class (k), e.g., in this example these may be the classes NBZ, SBZ, DBZ;
  • N represents the total number of measurements;
  • i represents the measurement number, in this case running from 1 to N.
  • each number of measurements may correspond to one level of wear prediction.
  • the above example provides an advantageous choice to compute confidences but other formulas may be used as well.
  • wear level and confidence may be represented as tuples, where wear levels are fixed levels and confidences float. Summing all confidences for all tuples, e.g., for all possible wear levels, gives 1. Wear level needs to be in one of the cases. The wear level of a single measurement is selected by selecting the corresponding highest confidence. The aggregated wear level is selected as the wear level that has the highest aggregated confidence; however, the wear level is still one of the original levels. Further examples are given herein.
  • the monitoring method is a synthetic lengthy body health and/or synthetic lengthy body condition monitoring method.
  • the level of wear may be monitored periodically or even continuously.
  • a graph of level of wear and/or confidence may be produced.
  • a warning may be generated if the graph shows changes, e.g., increase or decrease over a threshold, etc.
  • multiple diffractive X-ray data by rotating the synthetic lengthy body and/or by taking X-rays under multiple angles.
  • the X-ray sensor may be associated with a mechanism arranged to measure X-ray data in tangential fashion at different points radially along the rope, e.g., from the left part of the rope and from the right part, or at more than two points.
  • the machine learning model may be configured to receive the multiple data, or the multiple data may be provided to the model separately after which the results may be combined.
  • monitoring device 100 may comprise a recognition unit 120.
  • Recognition unit 120 may be configured to apply a recognition algorithm on the obtained sensor data. The obtained sensor data may be discarded if the obtained sensor data does not correspond to valid diffractive X-ray data of a synthetic lengthy body.
  • Recognition unit 120 is optional. For example, if the environment wherein the diffraction data is obtained is sufficiently under control, then recognition unit 120 is not necessary. However, it can be an advantage to discard bad diffraction data. It can happen that the sensor data received from the sensor is not correct data. For example, the X-ray source may be incorrectly aimed at the rope, or may be aimed at a different object.
  • the rope recognition algorithm may discard the data and/or warn against the bad data.
  • the rope recognition algorithm may verify that the synthetic rope is of the correct type. For example, if PE rope, e.g., a Dyneema rope is expected, the rope recognition algorithm can verify that the correct type rope is used. This can be important since the trained model is typically trained for a rope with a particular composition.
  • the intensity of the received sensor data can be computed. If the intensity is below a threshold a warning or discarding may be triggered.
  • the rope recognition algorithm can compute a discrepancy between a location of peaks in the obtained X-ray data and peaks expected for the composition of the rope.
  • the observed data will not have the diffraction peak known for a rope type, e.g., for a polyethylene rope. If the discrepancy, e.g., expressed as a number, exceed a threshold, this may trigger a warning and/or discarding of the data.
  • a warning can trigger an engineer to verify if the sensor is aligned correctly, or if the system is configured for the correct rope type, etc.
  • a problem encountered in experiments is whether the diffraction data is obtained from a cover of the rope instead of the rope material itself. If a rope cover is of a different material, then this will be visible in an unexpected location of diffraction peaks. Even if the cover is of the same material as the rope than the peak intensities would be lower, e.g., below a threshold. In either situation, a warning may be given, or the system may refuse to use to take the data into account for a level of wear estimation.
  • a rope is made using rope material.
  • Rope material in the context of the classification herein is the high strength synthetic filaments. In other words, the rope material is the load bearing member of the rope, i.e., not a possible cover, a possible coating etc.
  • Training system 101 may comprise a training unit 180 configured with a training algorithm that corresponds to the machine learnable model 160.
  • model 160 comprises a neural network
  • training unit 180 may comprise a backpropagation algorithm.
  • model 160 comprises an LDA model
  • training unit 180 may comprise the corresponding LDA training algorithm.
  • Training system 101 has access to a training data set 181.
  • training data set 181 may comprise multiple pairs of a diffractive X-ray data and a corresponding level of wear of the synthetic lengthy body from which the sensor data was obtained.
  • training unit 180 repeatedly iterates through the training set and repeatedly makes modification to the multiple parameters of machine learnable model 160 decreasing a distance between an actual prediction of model 160 and the desired prediction according to the training data set.
  • the machine learnable model Once the machine learnable model is sufficiently trained it may be installed in a computerto obtain a trained monitoring system.
  • One way to obtain training data is to repeatedly apply wear to a synthetic lengthy body.
  • measurements are taken during or in between the wear to obtain pairs of diffraction data and a level of wear.
  • Another approach, which is advantageous in practice is to wear different parts of the rope by different amounts. When the wear has been applied the parts of the rope with different amounts of wear can be measured to obtain training data for different levels of wear.
  • An advantage of this approach is that apply the wear can take place in a first setup until finish, after which in a second setup the measurements can be taken. Training data is thus obtained more efficiently.
  • NBZ no bent zone
  • SBZ single bent zone
  • DBZ double bent zone
  • the different levels of wear can be binned into one of multiple discrete classes, e.g., the classes no-bend, single-bend, double-bend, or the classes no bending, 50% bending and 100% bending, or the classes low wear, medium wear, high wear.
  • NBZ, SBZ, DBZ bending occurs a specified number of times per cycle; in this case 0, 1 , or 2 times per cycle.
  • these are used in a cyclic test to failure, which could have thousands of cycles.
  • the double bend zone is bent twice per cycle, which is double as much as the SBZ area.
  • a cyclic wear test may also impart wear due to tension in the wear, irrespective of the bending.
  • a wearing regime includes the different levels of wear used in the analyzing of the diffractive X-ray data and the classification attributed to it.
  • a wearing regime includes a number of classes of wear and the amount of wear corresponding to that class, i.e. a level of wear corresponding to that class.
  • the wearing regime included three classes: NBZ, SBZ and DBZ.
  • the wearing regime may be “no bending”, “50% bending” and “100% bending”. Where 100% bending corresponds to a broken rope.
  • the wearing continues until the rope breaks.
  • at least one class is the breaking level of the rope, i.e. the level of wear at which the rope breaks.
  • FIG. 3d schematically shows an example of an embodiment of a monitoring device 102 for a synthetic lengthy body.
  • Monitoring device 102 is similar to device 101 but takes clever advantage of the capability of machine learning models to accept multimodal data.
  • device 102 comprises a further sensor 113 configured to provide further sensor data 114.
  • the further data may be optical data and/or spectroscopy data.
  • the model is configured to determine a combined value 162 indicating a level of wear from the further sensor data and the diffractive X-ray data.
  • An advantage of this embodiment is that two otherwise independent indicators may be combined into a level of wear indication.
  • the model may be trained to accept multiple sensor modalities, e.g., using a training set comprising tuples with multiple sensor data, and a desired level of wear data.
  • an optical sensor may be used to obtain data regarding the surface of a synthetic lengthy body.
  • the data may be indicative of surface defects, surface abrasion, (damaged) filament pull-out, fiber misalignment and the like.
  • the diffractive X-ray data is indicative of internal defects, e.g., internal abrasion and melt. Combining the two sources of data increases the robustness and/or reliability of the monitoring device.
  • the further sensor may have a corresponding further source.
  • a source may be used for light, e.g., for visual inspection, e.g., a broad band light source, possibly using filters for spectroscopy, a narrow band light source, e.g., a laser, etc.
  • the further sensor data may be evaluated by a physical model, e.g., to provide a conservative prediction of wear, remaining lifetime, and the like.
  • the further sensor data may also be subject to a trained machine learned model.
  • the trained machine learned model may be the same model receiving the X-ray data, but may also be a separate model. In the latter case the outputs of the models may be combined.
  • the monitoring device may be configured to apply the machine learnable model to multiple processed X-ray data obtained from multiple parts from the same lengthy body. In particular, obtained from parts of the rope that are close, or that have seen the same wear.
  • the machine learnable model may be trained to accept two sets of diffractive X-ray data.
  • An advantage of that approach is that the model can learn to extract any information that resides in correlations between the two samples. For example, one could take a first sample from the outside of a rope, while a second sample may be taken from inside the rope.
  • the two samples may be a predetermined distance from each other, e.g., 10 centimeters, 1 meter, etc., along the lengthy body.
  • the samples may be close to each other on the same rope, e.g., within the same 0,5 meter length, preferably within the same 1 meter length, more preferably within the same 2 meter length along the longest axis of the synthetic lengthy body.
  • Another approach to using multiple samples is to apply the model to one sample at a time, but to combine the model results.
  • the model may provide level of wear data, possibly together with confidence data.
  • the multiple wear values and confidence values may then be aggregated into an aggregated wear value and/or aggregated confidence value.
  • the measurements obtained from sensor in the field may be sent, e.g., using a computer network, to a central database, e.g., in the cloud.
  • a central database e.g., in the cloud. This way a large collection of data can be obtained, which in turn may be used to train a model centrally. This can improve prediction for all in the field deployed testing machines.
  • the obtained diffractive X-ray data is associated with ground-truth information, e.g., the actual wear level of the corresponding rope.
  • ground-truth data such a data collection is useful, as it may be used to derive the typical progression of wear in a rope.
  • the data can be used to pre-train an autoencoder part of the model.
  • useful information may be obtained from a level of change in the rope compared to a previous measurement, etc. For example, one may detect in this way, if a rope, or part thereof, fails earlier than usual, e.g., for similar types of ropes and applications, say on other ships.
  • This information may also be used to give recommendations to a particular user, e.g., on rope care, e.g., avoiding wear to the rope. For example, by comparing the progression of wear in first rope to the progression of wear in one or more similar ropes used in similar applications, it can be established if the first rope has higher wear than expected. For example, one may give the recommendation that the sheaves need to be checked for damage, etc.
  • a lifetime may be attributed to the rope.
  • Such attributed lifetime to the rope, with a given safe use margin is typically in combination with the crane requirements amongst which a maximum load at which the crane in combination with that rope can be safely operated. This maximum load is as also referred to as crane rating.
  • An embodiment of the monitoring method for a synthetic lengthy body may comprise as an additional step generating an indication for further use of the synthetic lengthy body.
  • a load lifting device comprising a crane and a synthetic lengthy body arranged for mutual cooperation: provide a value to which the crane could temporarily be de-rated such that the synthetic lengthy body, such as a rope, is still safe for use with the de-rated crane.
  • a 150 metric ton crane may be de-rated to a 100 metric ton crane.
  • a rope which would no longer be safe to be used on that crane for, say, a 150 metric ton lift job may, e.g., still safely be used for a 100 metric ton lift job. After putting a new rope on the crane it can work again at full crane rating.
  • the present invention therefore provides a computer implemented monitoring method for a synthetic lengthy body having attributed lifetime with a given safe use margin, comprising obtaining diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in an X-ray sensor, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold, generating an indication for further use of the synthetic lengthy body, such as a remaining percentage of the attributed lifetime with a given safe use margin.
  • the monitoring method is arranged for a load lifting device, the rope having an attributed lifetime with a given safe use margin, e.g., connected with the load rating of the crane, comprising obtaining diffractive X-ray data corresponding to the rope, wherein the diffractive X-ray data has been obtained from the rope by directing X-ray radiation to at least part of the rope and sensing the diffracted X-ray radiation in an X-ray sensor, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the rope, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold, generating an indication for further use of the load lifting device, such as providing a new attributed rope lifetime with given safe use margin at a lower load rating (de-rating) for the crane.
  • communication interfaces may be selected from various alternatives.
  • the interface may comprise a network interface to a local or wide area network, e.g., the Internet, a storage interface to an internal or external data storage, a keyboard, an application interface (API), etc.
  • a network interface to a local or wide area network, e.g., the Internet
  • a storage interface to an internal or external data storage
  • keyboard e.g., a mouse
  • API application interface
  • Device 100 may have a user interface, which may include well-known elements such as one or more buttons, a keyboard, display, touch screen, etc.
  • the user interface may be arranged for accommodating user interaction for configuring the systems, training the model on a training set, or applying the system to new sensor data, etc.
  • Storage may be implemented as an electronic memory, say a flash memory, or magnetic memory, say hard disk or the like. Storage may comprise multiple discrete memories together making up the storage.
  • the storage may be cloud storage.
  • Devices 100, 102 may be implemented in a single device or in a system.
  • System 102 may be implemented in a single device.
  • a single device in an example of a system.
  • the system/de vices 100, 101 , 102 each comprise a microprocessor which executes appropriate software stored at the system; for example, that software may have been downloaded and/or stored in a corresponding memory, e.g., a volatile memory such as RAM or a non-volatile memory such as Flash.
  • the systems may, in whole or in part, be implemented in programmable logic, e.g., as field- programmable gate array (FPGA).
  • FPGA field- programmable gate array
  • the systems may be implemented, in whole or in part, as a so-called application-specific integrated circuit (ASIC), e.g., an integrated circuit (IC) customized for their particular use.
  • ASIC application-specific integrated circuit
  • the circuits may be implemented in CMOS, e.g., using a hardware description language such as Verilog, VHDL, etc.
  • systems/device 100, 101 , 102 may comprise circuits for the evaluation of neural networks.
  • a processor circuit may be implemented in a distributed fashion, e.g., as multiple sub-processor circuits.
  • a storage may be distributed over multiple distributed sub-storages.
  • Part or all of the memory may be an electronic memory, magnetic memory, etc.
  • the storage may have volatile and a non-volatile part.
  • Part of the storage may be read-only.
  • a computer implemented monitoring method for a synthetic lengthy body comprising synthetic filaments, the method comprising obtaining diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X- ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X- ray radiation in an X-ray sensor providing information about crystalline morphology of the filaments, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
  • Morphology herein is the shape, size, orientation, volume fraction of the crystals in the filaments, including their internal structure.
  • a computer implemented monitoring method if provided for a synthetic lengthy body comprising synthetic filaments, the method comprising obtaining diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X- ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X- ray radiation in an X-ray sensor, the diffractive X-ray data indicating a diffraction image, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
  • a diffraction image may be obtained from 1D information, including a diffraction pattern, such as obtained from an array I linear detector, or from 2D information.
  • 2D information includes a diffraction pattern (nonreduced) or diffractogram (reduced, e.g., integrated to 1 D).
  • a diffraction image includes a diffraction pattern and a diffractogram.
  • Method 300 may be computer implemented and comprises obtaining (310) diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in an X-ray sensor, providing (315) a trained machine learnable model, processing (320) the diffractive X-ray data for input to the machine learnable model, applying (325) the machine learnable model to the processed diffractive X-ray data, the machine learnable model being configured to receive the processed diffractive X-ray data and to generate a value quantifying a level of wear in the part of the lengthy body, generating (330) a warning signal if the level of wear indicated by the value exceeds a wear threshold.
  • Method 350 may be computer implemented and comprises obtaining (360) multiple diffractive X-ray data corresponding to multiple parts of one or more synthetic lengthy bodies, the multiple parts having been exposed to multiple levels of wear, wherein a diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffractive X-ray radiation in an X-ray sensor, obtaining (365) for the multiple X-ray data the level of wear to which the corresponding part of the lengthy body has been exposed, processing (370) the diffractive X-ray data for input to a machine learnable model, applying (375) a machine training algorithm corresponding to the machine learnable model to the multiple processed X-ray data and the corresponding multiple levels of wear to obtain a trained machine learnable model, the trained machine learnable model being configured to generate a value quantifying a level of wear in the part of the lengthy body
  • the monitoring and training method may be computer implemented methods. For example, accessing training data, and/or receiving input data may be done using a communication interface, e.g., an electronic interface, a network interface, a memory interface, etc. For example, storing or retrieving parameters may be done from an electronic storage, e.g., a memory, a hard drive, etc., e.g., parameters of the networks. For example, applying a model to data of the training data set, and/or adjusting the stored parameters to train the network may be done using an electronic computing device, e.g., a computer. The model either during training and/or during applying may have multiple parameters, e.g., at least 50, 100, 1000 parameters or more.
  • a communication interface e.g., an electronic interface, a network interface, a memory interface, etc.
  • storing or retrieving parameters may be done from an electronic storage, e.g., a memory, a hard drive, etc., e.g., parameters of the networks.
  • Embodiments of the method may be executed using software, which comprises instructions for causing a processor system to perform method 300 and/or 350.
  • Software may only include those steps taken by a particular sub-entity of the system.
  • the software may be stored in a suitable storage medium, such as a hard disk, a floppy, a memory, an optical disc, etc.
  • the software may be sent as a signal along a wire, or wireless, or using a data network, e.g., the Internet.
  • the software may be made available for download and/or for remote usage on a server.
  • Embodiments of the method may be executed using a bitstream arranged to configure programmable logic, e.g., a field-programmable gate array (FPGA), to perform the method.
  • FPGA field-programmable gate array
  • the presently disclosed subject matter also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the presently disclosed subject matter into practice.
  • the program may be in the form of source code, object code, a code intermediate source, and object code such as partially compiled form, or in any other form suitable for use in the implementation of an embodiment of the method.
  • An embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the processing steps of at least one of the methods set forth. These instructions may be subdivided into subroutines and/or be stored in one or more files that may be linked statically or dynamically.
  • Another embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the devices, units and/or parts of at least one of the systems and/or products set forth.
  • Figure 9a shows a computer readable medium 1000 having a writable part 1010 comprising a computer program 1020, the computer program 1020 comprising instructions for causing a processor system to perform a monitoring and/or training method, according to an embodiment.
  • the computer program 1020 may be embodied on the computer readable medium 1000 as physical marks or by magnetization of the computer readable medium 1000. However, any other suitable embodiment is conceivable as well.
  • the computer readable medium 1000 is shown here as an optical disc, the computer readable medium 1000 may be any suitable computer readable medium, such as a hard disk, solid state memory, flash memory, etc., and may be non-recordable or recordable.
  • the computer program 1020 comprises instructions for causing a processor system to perform said monitoring and/or training method.
  • FIG. 9b shows in a schematic representation of a processor system 1140 according to an embodiment of a monitoring and/or training device.
  • the processor system comprises one or more integrated circuits 1110.
  • the architecture of the one or more integrated circuits 1110 is schematically shown in Figure 9b.
  • Circuit 1110 comprises a processing unit 1120, e.g., a CPU, for running computer program components to execute a method according to an embodiment and/or implement its modules or units.
  • Circuit 1110 comprises a memory 1122 for storing programming code, data, etc. Part of memory 1122 may be read-only.
  • Circuit 1110 may comprise a communication element 1126, e.g., an antenna, connectors or both, and the like.
  • Circuit 1110 may comprise a dedicated integrated circuit 1124 for performing part or all of the processing defined in the method.
  • Processor 1120, memory 1122, dedicated IC 1124 and communication element 1126 may be connected to each other via an interconnect 1130, say a bus.
  • the processor system 1110 may be arranged for contact and/or contact-less communication, using an antenna and/or connectors, respectively.
  • processor system 1140 e.g., the monitoring and/or training device may comprise a processor circuit and a memory circuit, the processor being arranged to execute software stored in the memory circuit.
  • the processor circuit may be an Intel Core i7 processor, ARM Cortex-R8, etc.
  • the processor circuit may be ARM Cortex M0.
  • the memory circuit may be an ROM circuit, or a non-volatile memory, e.g., a flash memory.
  • the memory circuit may be a volatile memory, e.g., an SRAM memory.
  • the device may comprise a non-volatile software interface, e.g., a hard drive, a network interface, etc., arranged for providing the software.
  • the memory 1122 may be considered to constitute a storage device.
  • Memory 1122 may be an electronic memory. Various other arrangements will be apparent. Further, the memory 1122 may be considered to be “non-transitory machine-readable media.” As used herein, the term “non-transitory” will be understood to exclude transitory signals but to include all forms of storage, including both volatile and non-volatile memories.
  • the various components may be duplicated in various embodiments.
  • the processor 1120 may include multiple microprocessors that are configured to independently execute the methods described herein or are configured to perform steps or subroutines of the methods described herein such that the multiple processors cooperate to achieve the functionality described herein.
  • the various hardware components may belong to separate physical systems.
  • the processor 1120 may include a first processor in a first server and a second processor in a second server.
  • FIG. 10c shows the fraction of X-ray transmitted through UHMWPE against X-ray energy and thickness of the material.
  • the left y-axis show label 1210: X-Ray Energy (kV); the right y-axis show label 1230: Transmission; and the x-axis shows label 1220: Polyethylene Thickness (cm).
  • the X-ray energies of common anode materials are shown on the right.
  • the current experiment was done with a Cu anode at 8.04 keV with substrands of thickness 2 mm, where the transmission is approximately 10 %.
  • an x-ray energy of 20 keV or more for example, this could be realized with an Ag or a W anode.
  • FIG. 10a shows at A, a driving sheave at the top and a test sheave at the bottom. The driving sheave is driven alternatively in both directions to provide wear to the rope at the test sheave.
  • Figure 10a shows at B a side view of the test sheave. In figure 10b a groove can be seen for guiding the rope.
  • Figure 10a shows at panel C the test sheave, with a rope. Three regions are indicated marked: NBZ, SBZ and DBZ. These regions receive a different wearing regime.
  • NZ No bend zone
  • SBZ Single bend zone
  • Double bend zone Always on the test sheave- Estimated to have 0 % remaining lifetime after test
  • the remaining lifetimes have been estimated to demonstrate the effectiveness of the embodiment, though in an application the remaining lifetimes could be different. For example, more accurate remaining lifetime estimates may be obtained by making continuous rope out of an NBZ, SBZ or DBZ section and test till failure.
  • rope sections are cut from each of the three zones (NBZ, SBZ and DBZ).
  • a single strand was separated from each of the three zones.
  • Each of the 21 substrands were measured at 18 positions with wide-angle x-ray scattering (WAXS), for a total of 378 scans.
  • WAXS wide-angle x-ray scattering
  • the X-ray radiation went through 4 to 7 mm of rope material (UHMWPE), depending on the actual shape of the strand at the particular point.
  • Figure 10b shows at a) an entire rope section, at b) one of 12 strands from a rope, at c) all 7 sub-strands from a strand and at d) a single sub-strand.
  • the ropes used here is a braided rope and so a single strand will vary between an internal and external cross-sectional position along the fiber axis.
  • the strands are cut out so they both contain an external strand section, where the strand is positioned in the external part of the rope facing away from the test sheave, and an internal strand section where the strand is positioned internally in the rope. These zones are marked with EXT and INT in figure 10b.
  • WAXS data was measured on a Pilatus 300K detector (487 x 619 pixels). Data is azimuthally integrated to 200 points. Data is further pre-processed by normalizing each observation to sum to 1 , a log transformation and a standardization of each feature. X-ray energy was 8 keV. The azimuthally integration to 1 D was found to lose little information since the WAXS data is highly internally correlated and to have many regions without information.
  • Figure 10d shows mean 1 D WAXS curves from all sub-strands for each of the three zones represented by mean curves (solid lines), standard deviation is indicated with shading.
  • LDA Linear discriminant analysis
  • RLR Regularized Logistic Regression
  • rope condition can be predicted from low-energy WAXS data.
  • the best model reaches nearly 90% accuracy.
  • ML Machine Learning
  • Other classification models were successfully applied to solve the three-class classification problem, including: Random Forest (RF), Adaboost, and K nearest neighbors (KNN).
  • RF Random Forest
  • Adaboost Adaboost
  • KNN K nearest neighbors
  • RLR has been used to test whether including more measurements of the samples tested will yield higher classification accuracy. 21 substrands in total were sampled, each at 18 positions. In total 378 measurements were used. 3-fold cross- validation has been performed. The data is partitioned into 3 subsets, each containing observations from 7 substrands: 2 are used for training and 1 is used for testing. This is done in 3 folds so that each combination of the subsets for training is used.
  • the models are trained on individual measurements.
  • the models are evaluated using measurements from 1-9 positions on the same sub-strand (which of course always belong to the same class of wear treatment).
  • the model tests on each position individually.
  • the joint probability is calculated for each of the classes by multiplying the class probability estimates across the measurements. Finally, these are normalized, so that the class probability estimates sum to 1 .
  • the class with the highest probability is selected as the predicted class/ classification outcome.
  • the models are evaluated by the classification accuracy on the test set. This is the fraction of correct predictions on the test set. This is listed in the table below for each of the three cross-validation folds. Below the mean and variance of the classification accuracy is calculated. In the two lowest rows the mean and variance of the specificity and sensitivity are calculated. Each column uses a different number of observations to calculate the normalized joint probabilities before classifying the sample. The first column only uses 1 measurement, the next column uses two measurements to calculate the normalized joint probabilities and finally on the right side of the table nine measurements to calculate the normalized joint probabilities to do the classification.
  • a regressive model may be used instead of a classification model.
  • a remaining lifetime estimator is trained to produce a continuous estimate between 0 and 100 %.
  • the NBZ data and DBZ data is translated into 100 % remaining lifetime and 0 % remaining lifetime, respectively. This is because the rope sees almost no wear in the NBZ and reaches complete failure in the DBZ.
  • the SBZ data is estimated to have 50 % remaining lifetime. This regression model needs to make large interpolations, but this can be reduced by training with additional data.
  • the model is based on a regularized logistic regression classifier, wherein class probability estimates are extended to a continuous remaining lifetime prediction.
  • the class probability estimates are multiplied by the remaining lifetime of that class and summed to give the remaining lifetime estimate.
  • the RLR classifier can be extended to a continuous remaining lifetime predictor using the estimated class probabilities.
  • different regularized logistic regression models have been shown to yield a similar R 2 . Experiments showed that regularization much improves linear regression for predicting remaining lifetime.
  • Another way to extend the interpolation of the RLR classifier is to calculate the variance of the estimated probabilities in terms of remaining lifetime and not just the mean. This may be done using the following equation: a 2 - t c ) 2 • p c
  • an improved training of regression models uses an improved data collection scheme. For example, the time it took to break a rope, e.g., after running continuously on a sheave may be noted, after which other tests are run which are stopped at known fractions of that time (say at 10 %, 20 %, 30 %, 40 %, 50 %, 60 %, 70 %, 80 %, 90 %). Several WAXS measurements are made of each rope to use for testing purposes.
  • a constant temperature (water cooled) CBOS experiment is stopped at, e.g., 50% time to failure.
  • This effectively reduces the DBZ to an SBZ, the NBZ remains an NBZ, but the SBZ now shows an intermediate level of wear which may be associated to 50%*50% 25%, other intermediate times could be chosen to generate ropes of different wear level.
  • Data may be reduced as before using azimuthal integration and transforming the data as described for classification.
  • a regression problem may be carried out using the reduced WAXS data as independent data (predictors) and remaining lifetime as the dependent variable.
  • a non-linear model is used.
  • An advantage of a non-linear model is that it may outperform linear models, especially when larger amounts of data are available.
  • the evolution of morphology during mechanical wear, as most physical phenomena, is non-linear in nature.
  • Non-linear models are therefore likely to improve regression performance (R 2 ), when ropes of intermediate stages of wear are included in a regression analysis. It is highly likely that non-linear classifiers such as random forest regression, feed-forward neural networks or even convoluted neural networks (CNN), which are often used in image recognition tasks, would outperform linear models for larger amounts of training data.
  • CNN convoluted neural networks
  • a regularization method may be used to avoid overfitting. Especially, if there are many predictors compared to the number of observations. Common regularization methods are Lasso, Ridge or Elastic Net regularization.
  • WAXS measurements were performed with a wavelength of 0.1405 A -1 .
  • a CdTe Pilatus 2M detector was used and placed at a distance of 2.243 m.
  • the size of the X-ray beam was 100 x 100 ⁇ im.
  • the complete ropes were placed on a motorized stage and were scanned in steps of 300
  • FIG. 10e shows an example of an obtained WAXS image.
  • the left and bottom axes are in pixels, the right axis shows intensity (a.u.)
  • the WAXS curves have also been processed in terms of fitting with the aim of deconvoluting each measurement to fewer and less correlated variables. This was done using the python Imfit library. For each of the 140 observations each Bragg feature was fitted by a Pseudo-Voigt function and the amorphous background was fitted by a combination of a Pseudo-Voigt and a linear function.
  • the fitted amplitudes were used to calculate the fraction of material in monoclinic and orthorhombic crystalline phase and the overall crystallinity.
  • the fractions of material in crystalline phase was estimated by the integrated intensity of the WAXS curve cry from the crystalline peaks l cry divided by the total intensity I integrated:
  • Qmax is the maximum experimentally accessible Q value.
  • Qmin is the minimum experimentally accessible Q value.
  • Q ma x is 41.9 nnr 1 and Qmin is 9.0 nnr 1 .
  • f> 107.9 for polyethylene.
  • a ortho and b ortho were estimated from the orthorhombic 200 and 110 peaks respectively.
  • a mono and c mono were estimated from the monoclinic 200 and 001 peaks respectively.
  • the full width at half maximum (FWHM) of the peaks were used to estimate mean sizes of crystalline domains T hki perpendicular to a set of hkl planes.
  • K is the shape factor which in this work is set to 0.9 corresponding to a symmetric crystallite
  • p hkt is the FWHM in 20 measured in radians for the hkl reflection
  • e hkt is the Bragg angle for the hkl reflection.
  • LDA was chosen as a machine learnable model. LDA determines linear combinations of features which separate classes of data for classification. LDA projects the data X onto a C-1 dimensional sub-space, where C is the number of classes, while maximizing the ratio of between-class variance and within-class variance. With two classes this sub-space will thus be spanned by a single linear discriminant w which is determined in the following way.
  • these means and covariances are not known and have to be estimated with a training set using maximum likelihood estimators.
  • the maximum likelihood estimator of the covariance matrix may be unstable.
  • To stabilize the estimator one may introduce a shrinkage parameter y G [0,1] which shrinks the estimate of the covariance matrix towards diagonal matrix of variances.
  • a larger shrinkage parameter may be used when less training data is available compared to the number of predictors.
  • First LDA was performed using the 1 D WAXS data as input after normalization and centering by subtracting mean values for each Q-position.
  • the linear discriminant w is calculated using the training observations and Eq. 7.
  • Figure 10f shows 1 D WAXS curves calculated from 70 scans of a healthy (bottom) and 70 scans of a damaged (top) rope.
  • Figure 10g shows the linear discriminant coefficients from the first cross-validation fold. The areas corresponding to monoclinic and orthorhombic peaks are indicated with a shaded background. The higher the absolute value of a coefficient the higher the importance of the scattering signal at that Q-value in terms of distinguishing between healthy and damaged ropes. These coefficients are very similar across all three cross-validation folds.
  • LDA LDA was performed using fitted parameters.
  • the data was standardized by subtracting the mean and dividing by the standard deviation for each input parameter.
  • the linear discriminant is calculated using the training observations and Eq. 7.
  • the decision boundary is again calculated using Eq. 8.
  • LDA is able to classify all observations in both the training set and the test set correctly.
  • the test classification accuracies across all three cross-validation folds were 100 % which demonstrates that this predictive method based on fitted input parameters also generalizes to independent data.
  • the trained models distinguish between WAXS scans of the healthy and damaged rope using 1 D WAXS data and fitted parameters, respectively.
  • the signal- to-noise ratio was lowered by simulating and adding Gaussian noise. It was demonstrated that the noise could be increased by a factor of three while still maintaining a test classification accuracy of 100 % using either 1 D WAXS data or fitted parameters as input.
  • the mean test classification accuracies of both methods are above 75% even when the noise is increased by a factor of 10.
  • any reference signs placed between parentheses shall not be construed as limiting the claim.
  • Use of the verb "comprise” and its conjugations does not exclude the presence of elements or steps other than those stated in a claim.
  • the article “a” or “an” preceding an element does not exclude the presence of a plurality of such elements.
  • Expressions such as “at least one of’ when preceding a list or group of elements represent a selection of all or of any subset of elements from the list or group.
  • the expression, “at least one of A, B, and C” should be understood as including only A, only B, only C, both A and B, both A and C, both B and C, or all of A, B, and C.
  • the invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer.
  • the device claim enumerating several means several of these means may be embodied by one and the same item of hardware.
  • the mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
  • references in parentheses refer to reference signs in drawings of exemplifying embodiments or to formulas of embodiments, thus increasing the intelligibility of the claim. These references shall not be construed as limiting the claim.
  • the present invention includes the following embodiments Embodiment 1.
  • a computer implemented monitoring method for a synthetic lengthy body comprising obtaining diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in an X-ray sensor, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
  • Embodiment 2 A computer implemented monitoring method as in embodiment 1 , comprising providing a trained machine learnable model, processing the diffractive X-ray data for input to the machine learnable model, analyzing the diffractive X-ray data comprises applying the machine learnable model to the processed diffractive X-ray data, the machine learnable model being configured to receive the processed diffractive X-ray data and to generate the value quantifying a level of wear in the part of the lengthy body.
  • Embodiment 3 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the synthetic lengthy body is a synthetic rope or a synthetic chain.
  • Embodiment 4 A computer implemented monitoring method as in any one of the preceding embodiments, comprising obtaining an estimated remaining lifetime from the generated value.
  • Embodiment 5 A computer implemented monitoring method as in any one of the preceding embodiments, comprising applying the machine learnable model to multiple processed X-ray data obtained from multiple parts from the same lengthy body to obtain multiple values indicating multiple levels of wear, computing an aggregated level of wear estimation from the multiple values.
  • Embodiment 6 A computer implemented monitoring method as in any one of the preceding embodiments, comprising computing a confidence level for the level of wear or aggregated level of wear, generating a warning signal if the confidence level is below a confidence threshold and/or obtaining further X-ray data and recomputing the level of wear or aggregated level of wear.
  • Embodiment 7 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the lengthy body comprises multiple strands, the X-ray data being obtained by directing X-ray radiation to at least part of a strand isolated from the part of the lengthy body.
  • Embodiment 8 A computer implemented monitoring method as in any one of the preceding embodiments, wherein X-ray data is obtained by directing X-ray radiation tangentially to the lengthy body.
  • Embodiment 9. A computer implemented monitoring method as in any one of the preceding embodiments, comprising applying recognition algorithm on the obtained sensor data, the obtained sensor data being discarded if the obtained sensor data does not correspond to valid diffractive X-ray data of a synthetic lengthy body.
  • Embodiment 10 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the processing of the X-ray data comprises applying a dimension reduction algorithm.
  • Embodiment 11 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the wear comprises filament damage accumulation.
  • Embodiment 12 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the monitoring method is a synthetic lengthy body health and/or synthetic lengthy body condition monitoring method.
  • Embodiment 13 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the X-ray radiation has a photon energy from at least 5 keV, for example, the X-ray radiation having a photon energy in one of the ranges: from 5 keV up to 400 keV, from 5 keV up to 35 keV, e.g., of 8 keV; or from 5 keV up to 100 keV; or from at least 50 keV, e.g., in the range from 50 keV up to 100 keV, e.g., of 88 keV, from 100 keV up to 230 keV; or from 100 keV up to 400 keV.
  • the X-ray radiation has a photon energy from at least 5 keV, for example, the X-ray radiation having a photon energy in one of the ranges: from 5 keV up to 400 keV, from 5 keV up to 35 keV, e.
  • Embodiment 14 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the X-ray radiation has an energy sufficient to penetrate through the synthetic lengthy body.
  • Embodiment 15 A computer implemented monitoring method as in any one of the preceding embodiments, comprising obtaining further sensor data from a further sensor, wherein the further sensor data is one or more of optical data, acoustic data, ultrasonic data, emission data, and/or spectroscopy data, determining a combined value indicating a level of wear from the further sensor data and the diffractive X-ray data.
  • Embodiment 16 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the synthetic lengthy body is a UHMWPE rope, in particular a braided UHMWPE rope comprising multiple strands.
  • Embodiment 17 A computer implemented monitoring method as in any one of the preceding embodiments, wherein the X-ray radiation is line-focused onto the at least part of the synthetic lengthy body.
  • Embodiment 18 A computer implemented training method (350) for a monitoring system configured for a synthetic lengthy body, the training method comprising obtaining (360) multiple diffractive X-ray data corresponding to multiple parts of one or more synthetic lengthy bodies, the multiple parts having been exposed to multiple levels of wear, wherein diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffractive X-ray radiation in an X-ray sensor, obtaining (365) for the multiple X-ray data the level of wear to which the corresponding part of the lengthy body has been exposed, processing (370) the diffractive X-ray data for input to a machine learnable model, applying (375) a machine training algorithm corresponding to the machine learnable model to the multiple processed X-ray data and the corresponding multiple levels of wear to obtain a trained machine learnable model, the trained machine learnable model being configured to generate a value quantifying a level of wear in the part of the lengthy body, installing (380
  • Embodiment 19 Monitoring device for a synthetic lengthy body, the monitoring device comprising a sensor interface configured to obtain diffractive X-ray data from an X-ray sensor arranged for diffractive X-ray measurements corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in the X-ray sensor, a storage interface configured to provide a trained machine learnable model, processor subsystem configured for analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
  • a sensor interface configured to obtain diffractive X-ray data from an X-ray sensor arranged for diffractive X-ray measurements corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by
  • Embodiment 20 Monitoring device for a synthetic lengthy body as in embodiment 19, wherein the processor subsystem is configured for processing the diffractive X-ray data for input to the machine learnable model, applying the machine learnable model to the processed diffractive X-ray data, the machine learnable model being configured to receive the processed diffractive X-ray data and to generate a value quantifying a level of wear in the part of the lengthy body,
  • Embodiment 21 Monitoring device as in Embodiment 19 or 20 comprising the X- ray source and X-ray sensor, the lengthy body monitoring device comprising one or more spacers for spacing the lengthy body form the X-ray source and X-ray sensor while the lengthy body is submerged in water during operation of the X-ray source and X-ray sensor.
  • Embodiment 22 A load lifting device comprising a synthetic lengthy body, the load lifting device being arranged to lift a load with the synthetic lengthy body, and the monitoring device configured for the synthetic lengthy body as in any one of embodiments 19, 20 and 21 .
  • Embodiment 23 A training system for a synthetic lengthy body monitoring system, the training system comprising a training data interface configured for obtaining multiple diffractive X-ray data corresponding to multiple parts of one or more synthetic lengthy bodies, the multiple parts having been exposed to multiple levels of wear, wherein a diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffractive X-ray radiation in an X-ray sensor, and obtaining for the multiple X-ray data the level of wear to which the corresponding part of the lengthy body has been exposed, a processor interface configured for processing the diffractive X-ray data for input to a machine learnable model, applying a machine training algorithm corresponding to the machine learnable model to the multiple processed X-ray data and the corresponding multiple levels of wear to obtain a trained machine learnable model, the trained machine learnable model being configured to generate a value quantifying a level of wear in the part of the lengthy body, installing the trained machine learnable model in
  • Embodiment 24 A transitory or non-transitory computer readable medium (1000) comprising data (1020) representing instructions, which when executed by a processor system, cause the processor system to perform the method according to any one of embodiments 1-18.
  • data (1020) representing instructions which when executed by a processor system, cause the processor system to perform the method according to any one of embodiments 1-18.

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Abstract

Some of the disclosed subject matter relates to a computer implemented monitoring method for a synthetic lengthy body, e.g., a rope or a chain. A machine learnable model is applied to diffractive X-ray data obtained from the synthetic lengthy body. The machine learnable model receives the processed diffractive X-ray data generates a value quantifying a level of wear in the part of the lengthy body.

Description

MONITORING FOR A SYNTHETIC LENGTHY BODY
TECHNICAL FIELD
The invention relates to a computer implemented monitoring method for a synthetic lengthy body, a computer implemented training method for a monitoring system, a monitoring device for a synthetic lengthy body, a load lifting device, a training system for a synthetic lengthy body monitoring system, and a computer readable medium.
BACKGROUND
A synthetic lengthy body such as a rope is especially adapted to be used as load-bearing element in many applications, e.g., as lifting ropes. During use of a rope, bending may occur repeatedly, for example, over a sheave during a lifting job. When exposed to such frequent bending or flexing, a rope wears down, e.g., experiences wear, and may fail due to rope and/or filament damage. Such fatigue failure is often referred to as bend fatigue or flex fatigue. When wear exceeds a certain level, the synthetic lengthy body may need to be discontinued, for fear it may fail, e.g., break.
Synthetic lengthy body herein comprises synthetic filaments. An advantageous material choice for a synthetic lengthy body, such as a rope, is polyethylene (PE), in particular ultra-high-molecular-weight polyethylene (UHMWPE).
UHMWPE ropes, which herein are ropes made using UHMWPE filaments, are emerging as a strong and light-weight alternative to steel wire ropes in many areas such as heavy lifting, maritime applications, commercial fishing, aquaculture, wind, defence and deep sea operations. In these industries the condition of ropes and wires are regularly inspected to ensure safe operations. For steel wire ropes this is typically done using magnetic flux leakage techniques. During use synthetic lenghty bodies, such as UHMWPE ropes, are subject to wear, which is critical to detect. The International Organization for Standardization (ISO) and the world’s largest classification society DNV- GL define the standards for off-shore fiber ropes describing current monitoring methods based on visual inspections and counting of loading cycles. Visual inspection is, however, only indirectly sensitive to microscopic defects and internal abrasion is not visible. For example, Japanese patent JP6048603 with title ‘Method for determining deterioration of colored polyethylene fiber, and colored polyethylene fiber’, included herein by reference, provides a method to judge degradation of a polyethylene fiber. The known method comprises coloring a polyethylene fiber and judging the deterioration of the fiber from an observed degradation of the coloring.
There is a demand for more quantitative non-destructive testing (NDT) methods to monitor the condition synthetic length bodies, such as UHMWPE ropes.
SUMMARY
It would be advantageous to have an improved method of judging the wear of a synthetic lengthy body. A condition monitoring system for a synthetic lengthy body, such as a rope comprising UHMWPE filaments, is provided. For example, the system may predict a remaining lifetime of the synthetic length body, e.g., a remaining lifetime for use in a particular application such as lifting objects. For example, the system may generate a warning signal if the level of wear exceeds a pre-set wear threshold.
A system is proposed that uses diffractive X-ray data. It was found that diffractive X-ray data measured at a synthetic lengthy body contains information that correlates with the wear history of a said body, such as the wear history of a filament, a strand and/or a rope; in particular a synthetic lengthy body comprising UHMWPE, in particular made from UHMWPE. As a result, diffractive X-ray data is correlated to the level of wear and/or to remaining lifetime of such synthetic lengthy body. The system provides a quantitative model linking the crystalline morphology of the synthetic filaments in the synthetic lengthy body to mechanical properties of the rope. Moreover, X-ray data contains information from inside the material forming the synthetic length body, e.g., inside the synthetic filament, which is not possible to obtain from external inspection such as visual inspection or optical inspection. In addition, X-ray allows to take microscopic phenomena into account, which is not possible for visual inspection or for optical methods. In an embodiment, X-ray diffraction data is integrative; this means that information is not recorded locally, e.g., on the surface, but captured in an accumulative fashion from the bulk. The information is obtained over the whole length that the X-ray beam goes through the material. Moreover, an X-ray inspection device may be installed in a physical location which is inconvenient or dangerous for a visual inspector. Diffractive X-ray data is preferably Wide-angle X-ray scattering (WAXS) data. However, the diffractive X-ray data may also be medium angle x-ray scattering (MAXS) data, or an Ultra-Wide-Angle X-Ray scattering, or a small angle X-ray scattering. A range of angles can be used depending on the polymer system that is monitored. In an embodiment, the Wide angle X-ray scattering (WAXS) data corresponds to a d-spacing between 0.15 nm to 0.7nm, in particular corresponds to a d-spacing in the range from 0.15 nm to 0.7nm.
Synthetic ropes, such as ropes made from ultra-high-molecular-weight polyethylene (UHMWPE) filaments are replacing steel wire ropes in many applications and a non-destructive testing method to monitor their condition is of scientific and commercial interest. The term rope and synthetic rope are used interchangably herein, if a steel wire rope is meant this is specifically named a steel wire rope.
In this application wide-angle X-ray scattering (WAXS) combined with linear discriminant analysis (LDA) is demonstrated to be effective in a computer implemented monitoring method for a synthetic lengthy body to quantifying a level of wear. Such a method may be used to, e.g., generate a warning signal if the level of wear of the synthetic lengthy body exceeds a wear threshold.
In an aspect of the work herein wide-angle X-ray scattering (WAXS) is combined with linear discriminant analysis (LDA) and applied as a method to classify healthy and worn out UHMWPE ropes. Healthy and damaged 22 mm thick ropes have been studied using hard X-rays. Firstly it is demonstrated that WAXS scans of healthy and damaged ropes can be distinguished with 100 % cross-validated test classification accuracy using LDA; this is shown both with input data consisting of pre-processed 1 D WAXS data and with physical parameters retrieved by fitting the WAXS data. Secondly it is demonstrated that the classification performance is similar using the two forms of input data. Moreover, classification performance is robust to the presence of noise — for example, the noise in the signal could be increased by a factor of three while maintaining 100 % test classification accuracy across all three cross-validation folds.
A computer implemented monitoring method for a synthetic lengthy body is provided in which diffractive X-ray data is obtained for a synthetic lengthy body. For example, the synthetic lengthy body may be a synthetic rope, a synthetic belt or a synthetic chain made using a synthetic material. For example, the synthetic material may be polyethylene, e.g., UHMWPE or aromatic polyamide, also referred to as aramid. UHMWPE fibers are known under trademark Dyneema®, aramid fibers are known under trademarks Keylar® and Nomex®.
The diffractive X-ray data may be analyzed, and a value may be generated quantifying a level of wear in the corresponding part of the synthetic lengthy body. If the level of wear indicated by the value exceeds a wear threshold, then a warning signal may be generated.
The inventors found that the crystalline/amorphous structure in a synthetic rope changes with use, and that the diffractive X-ray data depends on the crystalline/amorphous structure. The diffracted X-ray radiation, e.g., as represented in the diffractive X-ray data, provides information about crystalline morphology of the filaments. In an embodiment, the diffractive X-ray data is dimensional data, e.g., 1- dimensional or 2-dimensional. It is envisioned, that one could even use 3-dimensional X- ray diffraction tomography. For example, the diffractive X-ray data may be image data. For example, the diffraction image may be obtained from 1 D information, e.g., from a diffraction pattern, such as obtained from an array or linear detector or from 2D information. The diffractive X-ray data may represent a diffraction image. 2D diffractive X-ray data may include a diffraction pattern (non-reduced) or diffractogram (reduced, e.g., integrated to 1 D). Thus, a diffraction image includes a diffraction pattern and a diffractogram. In particular, the inventors have found that wear, such as intra-filament damage, changes a structural property of the filament. This changed property is correlated with the bending history of a synthetic lengthy body, such as a rope made from UHMWPE filaments, this allows to non-destructively classify levels of wear.
By analyzing the diffractive X-ray data such intra-filament damage may be detected and expressed in a quantifying value. This is demonstrated herein for a rope made form UHMWPE filaments.
The external of a synthetic lengthy body is the outside, that is, that what you can see. Internal is inside the synthetic lengthy body, e.g., at strand, or filament level. Intra-filament wear results in a structural change inside the filament. It was not known in the art, how to quantify intra-filament wear using diffractive X-ray data.
One way to obtain analysis with high resolution is to apply a machine learnable model. A machine learnable model may be applied to the processed diffractive X-ray data, to generate a value quantifying a level of wear in the part of the synthetic lengthy body. Unfortunately, the relationship between diffractive X-ray data and the condition of the rope is highly non-linear and non-obvious. Wear in a synthetic rope is the result of a complex interaction between the single filaments that make up the internal structure of the rope, including, e.g., frictional sliding, heat, torsion of filaments, and so on. In particular, the state of the art does not provide quantitative models linking damage of the crystalline morphology to the decline in mechanical properties of the rope. It was not known in the art how to determine the condition of the rope given diffractive X-ray data. By applying a machine learnable model this correlation can be made explicit in a practical method.
Furthermore, a machine learnable model enables the use of large amounts of data of possibly inferior quality data. This in turns allows data to be collected in the field where conditions are not as well-defined as in a laboratory, and where the measurement time per point may be short. A machine learnable model further allows integrating multiple information sources, e.g., multiple diffractive X-ray data, from different parts of the rope, and/or further sensor data obtained from a different sensor modality, e.g., visual data, such a images taken by a camera, in particular a high speed camera, of the outside of the rope; optical or spectroscopy data; acoustic data such as data from acoustic emission or an acoustic-ultrasonic method; rope diameter data or a combination thereof.
The present invention therefore also relates a computer implemented monitoring method for a synthetic lengthy body, comprising as additional step obtaining further sensor data from a further sensor, wherein the further sensor data is optical data, acoustic data, such as acoustic emission or an acoustic-ultrasonic method.
Embodiments may be adapted to rope diameter, also referred to as rope thickness herein, in various ways. For example, one may select a suitable X-ray energy, e.g., typically higher for thicker ropes. Depending on the energy a scattering setup and geometry may be selected, so that a d-spacing range can be obtained, this is then a range of lengths in real space. This is further described herein. The obtained data may then be analyzed, e.g., classified using a machine learnable model.
In an embodiment, the monitoring device is based on a machine learnable model with input data obtained via an acoustic-ultrasonic method. The acousticultrasonic methods employ two ultrasonic/acoustics transducers where for example one is working as a transmitter the other as a receiver (detector). The transmitter and the receiver are placed spatially apart (preferably between 10 and 50cm) along the length of lengthy body. Transmitter and receiver are placed onto the synthetic lengthy body each with a spring loaded system and a viscous coupling fluid between the active surface of the transmitter/receiver, chosen to be inert to the lengthy synthetic body. For polyethylene, this may be silicon oil based or glycerin. For certain geometries of the synthetic lengthy body, it may be beneficial to use no highly viscous coupling fluid and use water or air instead. T ransmitter can work for example in the range of 1 -3 MHz, the receiver in the in a lower frequency of 100 to 375 kHz. Dual head transmitters/receivers can be used so that both locations can transmit and receive. The transmitter and receiver are attached to preamplifier, bandwidth-filter and amplifier to be converted into a machine-readable digital signal. To analyze the condition of the synthetic lengthy body with the machine learnable model, a burst pulse signal is introduced via the transmitter which will be conducted by the synthetic lengthy body to the location of the receiver and constitutes the data signal which can be processed in a similar fashion as the X-ray data by the machine learnable model, employing most of the same strategies of data integrity control, data reduction, data processing, classification, and training. The conductivity of sound depends on the crystalline/amorphous nature of the lengthy body and is altered by damage introduced by wear. These changes are non-linear and unobvious to the experimenter, but can be successfully analyzed by a machine learnable model to classify the wear level of a synthetic lengthy body.
In an embodiment the present disclosure provides a computer implemented monitoring method for a synthetic lengthy body, comprising
- obtaining acoustic emission data corresponding to the synthetic lengthy body, wherein the acoustic emission data has been obtained from the synthetic lengthy body by applying a load to at least part of the synthetic lengthy body which exceeds the historical load of said lengthy body and sensing the emitted sound in an ultrasonic transducer with an amplifier, the acoustic emission data indicating a time series of voltages,
- analyzing the acoustics emission data with a machine learnable model,
- generating a value quantifying a level of wear in the part of the lengthy body, and
- generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
In an embodiment, the monitoring device is based on a machine learnable model with input data from acoustics emission. To use input for the machine learning model based on acoustics emission, the synthetic lengthy body is clamped along its length over a certain distance (depending on the diameter of the lengthy body) for example over distance of 2 m. One or multiple ultrasonic transducers are placed onto the synthetic lengthy body each with a spring loaded system and a viscous coupling fluid between the active surface of the transmitter/receiver, chosen to be inert to the lengthy synthetic body. For polyethylene, this may be silicon oil based or glycerin. For certain geometries of the synthetic lengthy body, it may be beneficial to use no highly viscous coupling fluid and use water or air instead. A certain tension is then applied between the clamps which corresponds to 110% of the maximum load the rope has seen during its current lifetime. Given this increased maximum load the rope will release energy, among other in the form of ultrasonic sound, which is detected by the transducers. This ultrasonic data is obtained along the synthetic lengthy body at the location of the transducer and can be processed in a similar fashion as the X-ray data by the machine learnable model, employing most of the same strategies of data integrity control, data reduction, data processing, classification, and training. For a continuous scanning of the synthetic lengthy body, rollers with a differential speed can be used to obtain the required tension.
In an embodiment the present disclosure provides a computer implemented monitoring method for a synthetic lengthy body, comprising
- obtaining acoustics-ultrasonic data corresponding to the synthetic lengthy body, wherein the acoustics-ultrasonic data has been obtained from the synthetic lengthy body by coupling a sound wave to at least part of the synthetic lengthy body and sensing the conducted sound wave with a transducer with an amplifier, the acoustics-ultrasonic data indicating a time series of voltages,
- analyzing the acoustics-ultrasonic data with a machine learnable model,
- generating a value quantifying a level of wear in the part of the lengthy body, and
- generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
Integrating multiple information sources may be done by training a model to accept the multiple information sources as multiple inputs, or by training models separately for each information source, e.g., each sensor modality and to combine their model outputs. In an embodiment, confidence values may be obtained by the multiple models; in an embodiment, an aggregated level of wear indication may be derived from multiple levels of wear of the multiple models and possibly the confidence values. In an embodiment, a model may be trained on use-data, e.g., number of load cycles, load weights, etc., to produce a level of wear indicator and possibly a confidence value. This model output can also be combined with the sensor derived estimations.
A further advantage is that internal defects may be detected that are not visible from the outside, e.g., internal melt, internal abrasion, and intra-filament wear. In an embodiment, the level of wear may be expressed as predicted remaining lifetime. In an embodiment, the testing may be nondestructive. Experiments performed on embodiments were able to classify healthy and worn out UHMWPE ropes with 100 % or near 100%, both with higher and with lower X-ray energy levels. In an embodiment, the X-ray data is obtained by directing X-ray radiation tangentially to the synthetic lengthy body, possibly from multiple angles or under lengthy body rotation. An advantage of this approach is that thicker lengthy bodies, that is lengthy bodies having a larger equivalent diameter, and/or lower energy levels may be used to obtain the diffraction data.
For example, in an embodiment, a rotatable X-ray diffractor may be used; the rotatable X-ray diffractor comprising an X-ray source and a detector connected to a goniometer allowing the source and detector to be rotated around the rope in a controlled way. This has the advantage that the rope can remain stationary. Rotating the rope in the field can be an undesired operation, which can be avoided by rotating the X-ray diffractor. In particular, measuring from multiple angles may advantageously be combined with measuring X-ray diffraction data in a tangential fashion, e.g., from a side of the rope. For example, in an embodiment tangential X-ray diffraction data may be obtained from multiples sides, e.g., from a left and right side of the rope, or from a left, bottom, right, and up-side. Measuring from multiple angles provides additional information about the rope; This can reduce the uncertainty, and is especially advantageous in cases with non-uniform wear.
From some of the sensor outputs, in particular, from diffractive X-ray data, but also, say, from spectroscopy data, one can verify that the data was obtained from a valid synthetic lengthy body, or, say, from a synthetic lengthy body of the right type, say, the right material. Outside of laboratory conditions, it may happen that accidentally, data is obtained from other materials, say from a cover of a synthetic lengthy body, or from a synthetic lengthy body of the wrong type, e.g., wrong material, given the model, such as metal or filament synthetic material (e.g., aramid if the rope is made using UHMWPE filaments). If left unattended, the model may produce a level of wear value for such data which may be completely unreliable. In an embodiment, a recognition algorithm is applied on the obtained sensor data, e.g., to discard the obtained sensor data if it does not correspond to the expected synthetic lengthy body, e.g., to PE material.
In an embodiment, the machine learnable model is trained for multiple different types of rope, and/or for different applications. For example, in an embodiment data indicating the type of rope is provided as an input to the machine learnable model. For example, additional information to the machine learning algorithm may include dimensions of the rope, e.g., rope diameter, rope type, e.g., rope braid type. This has the advantage that training material from different types of ropes can be better combined to train a single model. Although the model may be able to differentiate the different types of ropes from the diffraction data itself, learning will proceed faster and/or with more accuracy if the information is provided to the model.
A monitoring device may be installed in a load lifting device, e.g., a crane. For example, a typical embodiment of a load lifting device, such as a crane, may comprise a sheave and a synthetic lengthy body, such as a rope, arranged for mutual cooperation. An aspect of the invention further concerns a monitoring device for a synthetic lengthy body. An aspect of the invention concerns a training method, or training system, for training the machine learnable model used in a monitoring method or device.
In an embodiment, additional information for the model comprises information on the original load rating of rope/crane. From X-rays the model may determine, say, by what percentage loading capacity is decreased. Such estimates may also be computed from the output of the model, instead of having the model perform the computation. For example, a decreased rating may be provided, say as a percentage of the full rating, or as a new rating.
The monitoring device and training system may be implemented in a computer. An embodiment of the method may be implemented on a computer as a computer implemented method, or in dedicated hardware, or in a combination of both. Executable code for an embodiment of the method may be stored on a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc. Preferably, the computer program product comprises non-transitory program code stored on a computer readable medium for performing an embodiment of the method when said program product is executed on a computer.
In an embodiment, the computer program comprises computer program code adapted to perform all or part of the steps of an embodiment of the method when the computer program is run on a computer. Preferably, the computer program is embodied on a computer readable medium. BRIEF DESCRIPTIONS OF DRAWINGS
These and other aspects of the invention are apparent from and will be elucidated with reference to the embodiments described hereinafter. In the drawings,
Fig. 1 schematically shows an example of an embodiment of a device to determine tensile properties of a UHMWPE filament,
Fig. 2 schematically shows an example of an embodiment of a synthetic lengthy body, the synthetic length body being a synthetic drive chain,
Fig. 3a schematically shows an example of an embodiment of a monitoring device for a synthetic lengthy body,
Fig. 3b schematically shows an example of an embodiment of a monitoring device for a synthetic lengthy body,
Fig. 3c schematically shows an example of an embodiment of training system for a synthetic lengthy body monitoring system,
Fig. 3d schematically shows an example of an embodiment of a monitoring device for a synthetic lengthy body,
Fig. 3e schematically shows an example of an embodiment of a monitoring method for a synthetic lengthy body monitoring system,
Fig. 3f schematically shows an example of an embodiment of a training method for a synthetic lengthy body monitoring system,
Fig. 4 schematically shows an example of an embodiment of a load lifting device,
Fig. 5a schematically shows an example of an embodiment of a monitoring system for a synthetic lengthy body, in a side view,
Fig. 5b schematically shows an example of an embodiment of a monitoring system for a synthetic lengthy body, in a top view,
Fig. 5c schematically shows an example of a detail of an embodiment of a monitoring system for a synthetic lengthy body, in a top view,
Fig. 5d schematically shows an example of a detail of an embodiment of a monitoring system for a synthetic lengthy body, in a top view,
Fig. 6a schematically shows an embodiment of a synthetic rope,
Fig. 6b schematically shows directing an X-ray beam to the rim of a synthetic lengthy body,
Fig. 7 schematically shows an example of an embodiment of lengthy body,
Fig. 8 schematically shows an example of an embodiment of lengthy body. Fig. 9a schematically shows a computer readable medium having a writable part comprising a computer program according to an embodiment,
Fig.9b schematically shows a representation of a processor system according to an embodiment,
Fig. 10a shows an experimental arrangement to apply wear to a rope,
Fig. 10b shows a disassembling of a rope,
Fig. 10c shows a fraction of X-ray transmitted through UHMWPE against X- ray energy in keV and thickness of the synthetic material in cm, the contour lines 0.250, 0.500 and 0.750 indicate 25%, 50% and 75% X-ray transmission, respectively. The scale bar on the right indicates X-ray transmission.
Fig. 10d shows mean 1 D WAXS curves,
Fig. 10e shows an example of a high energy WAXS image of a synthetic rope, Fig. 10f shows mean 1 D WAXS curves,
Fig. 10g shows linear discriminant coefficients,
It should be noted that items which have the same reference numbers in different figures, have the same structural features and the same functions, or are the same signals. Where the function and/or structure of such an item has been explained, there is no necessity for repeated explanation thereof in the detailed description.
Reference signs list
The following list of references signs is provided for facilitating the interpretation of the drawings 1 , 2, 3a, 3b, 3c, 3d, 4, 9a and 9b, and shall not be construed as limiting the claims.
100,102 a monitoring device
101 a training system
110 an X-ray sensor
111 an X-ray source
112 diffractive X-ray data
113 a further sensor
114 further sensor data
120 a recognition unit
130 a processor system 140 a storage
150 a communication interface
160 a machine learnable model
161 ,162 a quantifying value
170 a post-processing device
180 a training unit
181 training data set
200 a synthetic lengthy body
800 a load lifting device
810 a monitoring device
820 a synthetic lengthy body
830 a load
121 , 122 a clamp
123 jaw faces
124 ceramic pins
125 a filament F Filament gauge length D downward direction
1000 a computer readable medium
1010 a writable part
1020 a computer program
1110 integrated circuit(s)
1120 a processing unit
1122 a memory
1124 a dedicated integrated circuit
1126 a communication element
1130 an interconnect
1140 a processor system
1210 X-Ray Energy (kV)
1220 Polyethylene (PE) Thickness (cm)
1230 Transmission DESCRIPTION OF EMBODIMENTS
While the presently disclosed subject matter is susceptible of embodiment in many different forms, there are shown in the drawings and will herein be described in detail one or more specific embodiments, with the understanding that the present disclosure is to be considered as exemplary of the principles of the presently disclosed subject matter and not intended to limit it to the specific embodiments shown and described.
In the following, for the sake of understanding, elements of embodiments are described in operation. However, it will be apparent that the respective elements are arranged to perform the functions being described as performed by them. Further, the subject matter that is presently disclosed is not limited to the embodiments only, but also includes every other combination of features described herein or recited in mutually different dependent claims.
Lengthy bodies, in particular ropes and chains
Embodiments relate to the monitoring of synthetic lengthy bodies. Examples of such synthetic lengthy bodies include without limitation a rope, a belt, a round sling, a splice, and synthetic chain amongst others. Lengthy bodies may be especially adapted to be used as load-bearing element in many applications such as lifting ropes. The term lengthy body includes but is not limited to a strand, a cable, a cord, a rope, a belt, a sling, a ribbon, a strip, a hose, and a tube. Synthetic lengthy bodies herein comprise high strength synthetic filaments.
By fiber is herein understood an elongated body, the length dimension of which is much greater than the transverse dimensions of width and thickness. The term fiber herein includes a filament, such filament may have a regular or irregular crosssection.
A filament is an elongated body, the length dimension of which is much greater than the transverse dimensions of width and thickness. Fibers may have continuous lengths, known in the art as filaments or continuous filaments, or discontinuous lengths, known in the art as staple fibers. A yarn for the purpose of the invention is an elongated body comprising at least two individual filaments. The filaments in the yarn may be twisted or untwisted, preferably the filaments of a yarn are untwisted.
By elongated herein is understood the length dimension being much greater than the transverse dimensions of width and thickness. Preferably said length dimension is at least 10 times, more preferably at least 20 times even more preferably at least 50 times and most preferably at least 100 times greater than the width or thickness dimension whichever is larger.
A lengthy body herein is herein understood an elongated body, the length dimension of which is much greater than the transverse dimensions of width and thickness or diameter. Preferably said length dimension is at least 10 times, more preferably at least 20 times even more preferably at least 50 times and most preferably at least 100 times greater than the width or thickness dimension whichever is larger.
The transverse dimensions of a lengthy body, such as a rope, are herein also referred to as width and thickness of the lengthy body and referred to as width and thickness of the rope. For a circular rope, that is a rope having a round diameter, Tz and Ty (see figure 6a) are substantially the same and are typically referred to as the diameter of the rope. Such rope would herein have a thickness of Tz. Rope thickness herein refers to rope diameter.
Typical applications of ropes and belts involve applications in which repeated bending occurs, amongst which bend-over-sheave applications. During such applications, the rope is frequently pulled over drums, bitts, pulleys, sheaves, etc., amongst others, resulting in rubbing and bending of the rope. When exposed to such frequent bending or flexing, a rope may fail due to rope and filament damage. Such fatigue failure is often referred to as bend fatigue or flex fatigue.
Typical applications of chains include storing, securing, such as securing a roll on/off dumpster to a dumpster hauling truck or freight to commercial trucks, flat bed trailers, lashing and tie down for handling and transporting cargo, in lifting and hoisting, logging, hauling and rigging, propulsion and driving, mooring, cargo-hold of an aircraft or naval ship and the like.
A synthetic chain herein typically comprises a plurality of interconnected chain links wherein at least a part of the links comprises high strength synthetic filaments. A synthetic chain may be a chain comprising a plurality of interconnected chain links wherein each link comprises high strength synthetic filaments, such as UHMWPE filaments. A synthetic chain is typically suitable to moor or anchor boats, to lash cargo in road, rail, water and air transportation and suitable for conveying, hoisting, suspending and lifting applications.
A rope in the context of the present invention is an elongated body having a length much larger than its lateral dimensions of, for example, width and thickness or diameter. The rope to be used in accordance with the invention may have a cross-section which is circular, rounded or polygonal or combination thereof. By diameter of the rope is herein understood the largest distance between two opposite locations on the periphery of a cross-section of the rope. The rope may have a cross-section that is about circular or round, but also an oblong cross-section, meaning that the cross-section of a tensioned rope shows a flattened, oval, or even an almost rectangular form. Such oblong cross-section preferably has an aspect ratio, e.g., the ratio of the larger to the smaller diameter (or width to thickness ratio), in the range of from 1.2 to 4.0, preferably in the range of 1.2 to 2.0.
A rope in the context of the present invention may include a belt having a length much larger than its transversal dimensions of e.g., width and thickness. An example of a belt includes a woven strip of yarns, a flexible band or strap and a loop of flexible material which may be used to link two or more deflection components such as sheaves, most often parallel arranged deflection components.
The rope in the context of the present invention may include multiple parallel ropes, having at least two interconnected ropes. In an aspect the cross-section of a tensioned rope herein is the cross section of the rope under a load of 300 MPa.
For the ropes with oblong cross-section, it is more accurate to define its size as a round rope with an equivalent diameter; that is the diameter of a circular rope of same mass per length as the non-round rope. The rope may have an equivalent diameter that varies between wide limits, for example, depending upon the operation conditions and size of the entity in which it is used, such as a crane or an airborne wind energy system. The diameter of a rope in general, however, is an uncertain parameter for measuring its size, because of irregular boundaries of ropes defined by the strands. A more concise size parameter is the linear density of a rope, also called titer or linear weight; which is its mass per unit length. The titer can be expressed in kg/m, but often the textile units denier (g/9000 m) or dtex (g/10000 m) are used. Diameter and titer are interrelated according to the formula 0 = (T/(1O*p*v))05, wherein T is the titer (dtex), d is the diameter (mm), p is the density of the filaments (kg/m3), and v is a packing factor (normally between about 0.7 and 0.9). It is noted that with formula herein equation is meant. Nevertheless, it is still customary in the rope business to express rope size in diameter values. The rope may have an equivalent diameter in the range of 0.1 mm to 5 mm, for example, for use in robotic applications. In another aspect, the rope is a rope having an equivalent diameter in the range from 5 mm to 20 mm. The rope may have an equivalent diameter in the range from 5 mm to 50 mm, for example, for use in an Airborne Wind Energy system. Preferably, the rope is a heavy-duty rope having an equivalent diameter of at least 20 mm, more preferably at least 30 mm, 40 mm, 50 mm, or even at least 60 mm. The rope typically has an equivalent diameter of up to about 350 mm, in an aspect of up to about 300 mm, in an aspect of up to about 250 mm.
A thicker rope or thick rope herein is typically referring to a UHMWPE rope having an equivalent rope diameter in the range from 15 mm to 350 mm. A thinner rope or thin rope herein is referring to a UHMWPE rope having an equivalent rope diameter in the range from 1 mm to 15 mm.
Typically, the amount of synthetic material the X-ray radiation has to travel through and the photon energy of the X-ray radiation source are adjusted to each-other such that the signal-to-noise level is sufficient to allow for analyzing the diffractive X-ray data, and generating a value quantifying a level of wear in the part of the lengthy body. For example, if X-ray diffraction data is desired to be obtained from a thicker rope it may be done via directing the X-ray radiation tangentially to the rope or by using a higher X- ray energy if applicable. A suitable way to select a location on the lengthy body to aim the X-ray radiation beam at, also referred to herein as irradiation zone, is to measure the amount of X-ray transmission through the lengthy body when irradiated at said location. In an aspect if a particular X-ray source results in < 75% transmission of the X-ray radiation through the center of the lengthy body a tangential path is preferred.
For example, if a synthetic lengthy body, such as a rope, is made from UHMWPE filaments, and a Tungsten (W) X-ray source results in < 75% transmission of the X-ray radiation through the rope, then tangential irradiation is preferred. The photon energy of W is 59.3 kV For W: 59.3 kV this would be equivalent to ca. 15 mm UHMWPE, as can be deduced from Figure 10c. Therefore, if a rope is made using UHMWPE filaments and a W X-ray source is used, the skilled person may prefer to apply tangential irradiation if the diameter of such rope is 15 mm or more. The skilled person may then for example direct the radiation to more towards the rim of the rope, such that the primary beam, i.e., the portion of the radiation that is not diffracted, has to travel through ca 15 mm of UHMWPE rope material.
If a different X-ray source is used (anode X-ray material) this may be applied analogously: using the 75% transmission provides a distinction between a thick and a thin rope, that is between tangentially and through rope irradiation. If measuring time is not limiting factor the skilled person may decide to measure longer at a lower transmission threshold e.g., 1.5x longer using a 50% transmission of the X-ray radiation threshold. Generally, a higher transmission is preferred, but the transmission threshold can be adjusted as long as multiple scattering does not become predominant in the X- ray diffraction data. In cases where the transmission threshold has to be lowered to levels where multiple scattering becomes predominant, it is advisable to acquire the training data at similar conditions.
For example, a rope made from aramid filaments would be considered a thick rope with respect to a Tungsten (W) X-ray source herein if it would result in < 75% transmission of the X-ray radiation through the rope. For W: 59.3 kV this would be equivalent to ca. 11 mm aramid. Herein this a translated to a rope made from aramid filaments having diameter of 11 mm. So, if a rope is made using aramid filaments and a W X-ray source is used, the skilled person may prefer to apply tangential radiation if the diameter of such rope is 11 mm or more.
A synthetic lengthy body, such as a rope or a belt ora chain, herein comprises high strength synthetic filaments. These synthetic filaments have a filament tenacity of at least 1.0 N/tex, preferably of at least 1 .2 N/tex, more preferably at least 1 .5 N/tex, even more preferably at least 2.0 N/tex, yet more preferably at least 2.2 N/tex and most preferably at least 2.5 N/tex. If the high strength filaments are UHMWPE filaments, said UHMWPE filaments preferably have a filament tenacity of at least 1 .8 N/tex, more preferably of at least 2.5 N/tex, even more preferably at least 3.0 N/tex and most preferably at least 3.5 N/tex. In an embodiment, the filament tenacity of the UHMWPE filaments is at most 4.5, at most 5.0, at most 5.5, or at most 7 N/tex; for example, in an embodiment, the filament tenacity is from 1 .5 N/tex up to 7 N/tex, or from 2.0 N/tex up to 5.5 N/tex. Preferably the high strength filaments have a filament tensile modulus of at least 30 N/tex, more preferably of at least 50 N/tex, most preferably of at least 60 N/tex. Preferably the UHMWPE filaments have a filament tensile modulus of at least 50 N/tex, more preferably of at least 80 N/tex, most preferably of at least 100 N/tex.
The filament tenacity and tensile modulus may be determined using the method Tensile properties of UHMWPE filaments as described herein in an analogous manner.
In an embodiment of the computer implemented monitoring method according to the invention, the synthetic lengthy body comprises synthetic UHMWPE filaments having a filament tenacity of at least 1 .5 N/tex.
In an embodiment the fraction of material of the synthetic filaments in the synthetic lengthy body in crystalline phase Xc[%] , i.e. Xc in units of percent, is at least 60% as defined by (1 a). In an aspect the fraction of material in the crystalline phase Xc[%] is at least 80% as defined by (1a). In an aspect the fraction of material in the crystalline phase Xc[%] is at least 90% as defined by (1a). For example, in an embodiment, the UHMWPE synthetic filaments in the synthetic lengthy body have an Xc[%] of at least 60% as defined by (1 a) and a filament tenacity of at least 1 .5 N/tex. In another embodiment, the UHMWPE synthetic filaments in the synthetic lengthy body have an Xc[%] of at least 80% as defined by (1 a) and a filament tenacity of at least 2.0 N/tex. In an aspect of the computer implemented monitoring method according to the invention, the generating a value quantifying a level of wear includes analyzing a change in the crystal structure, preferably a change in the amount of monoclinic crystal phase, more preferably an increase of the amount of monoclinic crystal phase.
In an embodiment, the synthetic lengthy body comprises synthetic UHMWPE filaments having a filament tenacity of at least 1.5 N/tex, and generating a value quantifying a level of wear includes analyzing a change in the crystal structure, preferably a change in the amount of monoclinic crystal phase, more preferably an increase of the amount of monoclinic crystal phase.
In the experiments described herein, using UHMWPE filaments, no indicative change in the crystalline phase Xc[%] was observed that was suitable to generate a value quantifying a level of wear in the part of the lengthy body.
Ropes comprising the high strength filaments may provide high strength. Therefore, the embodiments of the present invention preferably include a rope wherein the rope has a tenacity of at least 0.50 N/tex, preferably the rope has a tenacity of at least 0.60 N/tex, more preferably of at least 0.70 N/tex, even more preferably 0.80 N/tex and most preferably at least 1 .00 N/tex. The term rope system herein includes tether systems. In a further embodiment of the invention, the strength member has a tenacity of at least 0.9 N/tex, preferably at least 1.1 N/tex, more preferably at least 1 .3 N/tex and most preferably at least 1.5 N/tex. Break force may be determined by means of tensile testing (According to, e.g., ISO 2307).
Preferably the ropes have high tenacity and high diameters. The combination of these features provides ropes, including tethers, with a break strength, also called minimum break load (MBL) of at least 10 kN, more preferably of at least 50 kN and most preferably of at least 100 kN. The MBL may be obtained by testing according to ISO 2307, whereby the tenacity of the rope is calculated by dividing said MBL by the titer of the rope. Preferably the high strength synthetic filaments are filaments manufactured from and hence comprising a polymer chosen from the group consisting of polyamides and polyaramides, e.g., poly(p-phenylene terephthalamide) (known as Kevlar®); polytetrafluoroethylene) (PTFE); poly{2,6-diimidazo-[4,5b-4’,5’e]pyridinylene-1 ,4(2,5- dihydroxy)phenylene} (known as M5); poly(p-phenylene-2, 6-benzobisoxazole) (PBO) (known as Zylon®); liquid crystal polymers (LCP); poly(hexamethyleneadipamide) (known as nylon 6,6), poly(4-aminobutyric acid) (known as nylon 6); polyesters, e.g., polyethylene terephthalate), poly(butylene terephthalate), and poly(1 ,4 cyclohexylidene dimethylene terephthalate); polyvinyl alcohols; and also polyolefins e.g., homopolymers and copolymers of polyethylene and/or polypropylene. The preferred synthetic filaments are selected from polyaramide filaments and high or ultra-high molecular weight polyethylene (HMWPE or UHMWPE) filaments. Preferably the HMWPE filaments are melt spun and the UHMWPE filaments are gel spun, e.g., sold in the form of yarns manufactured by DSM Performance Materials, NL (known as Dyneema®). In an aspect the synthetic filaments are e-PTFE filaments (known as Omnibend®). Liquid crystal polymer (LCP) filaments are known as Vectran®.
In embodiments, the synthetic filaments are polyolefin filaments having a filament tenacity of at least 1.5 N/tex, preferably polyethylene, and/or polypropylene filaments having a filament tenacity of at least 1 .5 N/tex. In a preferred embodiment, the synthetic filaments are ultra-high molecular weight polyethylene (UHMWPE) filaments, more preferably gel spun UHMWPE filaments. In a further aspect, at least 50 wt%, more preferably at least 80 wt% and even more preferably at least 90 wt% and most preferably all of the high strength synthetic filaments present in the synthetic lengthy body are UHMWPE filaments.
In a preferred embodiment of the computer implemented monitoring method, the synthetic length body is a rope comprising ultra-high molecular weight polyethylene (UHMWPE) filaments, more preferably gel spun UHMWPE filaments. In a further aspect, at least 50 wt%, more preferably at least 80 wt% and even more preferably at least 90 wt% and most preferably all of the high strength synthetic filaments present in the rope are UHMWPE filaments.
In an embodiment of the computer implemented monitoring method, the synthetic lengthy body comprises multiple synthetic polyolefin filaments having a filament tenacity of at least 1.5 N/tex, preferably polyethylene, and/or polypropylene filaments having a filament tenacity of at least 1 .5 N/tex. In an embodiment of the computer implemented monitoring method, the synthetic lengthy body comprises synthetic UHMWPE filaments having a filament tenacity of at least 1 .5 N/tex.
The tenacity of polypropylene filaments is typically at most 3.0 or 2.0 N/tex. In embodiments, the polypropylene filaments have a tenacity in the range from 1 .5 N/Tex to 2.0 N/Tex. The polypropylene filament tenacity may be determined using the method Tensile properties of UHMWPE filaments as described herein in an analogous manner.
In a preferred embodiment of the synthetic rope, the rope comprises ultra- high molecular weight polyethylene (UHMWPE) filaments, more preferably gel spun UHMWPE filaments. In a further aspect, at least 50 wt%, more preferably at least 80 wt% and even more preferably at least 90 wt% and most preferably all of the high strength synthetic filaments present in the rope are UHMWPE filaments.
In an embodiment the rope is used in a tethered system, said rope comprising load carrying primary strands, said strands comprising high strength yarns, wherein these yarns comprise ultra-high molecular weight polyethylene filaments. In a further embodiment, at least 50 wt%, more preferably at least 80 wt% and even more preferably at least 90 wt% and most preferably all of the filaments present in the yarns are UHMWPE filaments.
Preferably the UHMWPE present in the UHMWPE filaments has an intrinsic viscosity (IV) of at least 3 dL/g, more preferably at least 4 dL/g, most preferably at least 5 dL/g. Preferably said IV is at most 40 dL/g, more preferably at most 30 dL/g, more preferably at most 25 dL/g. The IV may be determined according to ASTM D1601 (2004) at 135°C in decalin, the dissolution time being 16 hours, with BHT (Butylated Hydroxy Toluene) as anti-oxidant in an amount of 2 g/l solution, by extrapolating the viscosity as measured at different concentrations to zero concentration. Examples of gel spinning processes for the manufacturing of UHMWPE filaments are described in numerous publications, including WO 01/73173 A1 , EP 1 ,699,954 and in “Advanced Fibre Spinning Technology”, Ed. T. Nakajima, Woodhead Publ. Ltd (1994), ISBN 185573 182 7.
In an aspect the UHMWPE comprises short chain branches (SCB) which originate from a co-monomer present in the UHMWPE wherein the co-monomer is preferably selected from the group consisting of alpha-olefins with at least 3 carbon atoms, cyclic olefins having 5 to 20 carbon atoms and linear, branched or cyclic dienes having 4 to 20 carbon atoms. An alpha-olefin refers to an olefin with terminal unsaturation having 3 or more carbon atoms, preferably from 3 to 20 carbon atoms. Preferred alpha- olefins include linear mono-olefins such as propylene, butene-1 , pentene-1 , hexene-1 , heptene-1 , octene-1 and decene-1 ; branched mono-olefins such as 3-methyl butene-1 , 3-methyl pentene-1 and 4-methyl pentene-1 ; vinyl cyclohexane, and the like. Alphaolefins may be used alone, or in a combination of two or more.
In a preferred embodiment, the alpha-olefin has between 3 and 12 carbon atoms. Even more preferably the alpha-olefin is selected from the group consisting of propene, butene-1 , hexene-1 , octene-1. Most preferably propene, butene-1 , hexene-1 are present as co-monomer in the UHMWPE. The applicant found that these alphaolefins may readily copolymerize and may show optimized strongest effect on creep lifetime properties.
In an aspect the UHMWPE comprises at least 0.3 short chain branches per thousand total carbon atoms (SCB/1000TC), more preferably at least 0.4 SCB/1000TC and most preferably at least 0.5 SCB/1000TC. The co-monomer content of the UHMWPE is not particularly limited but for production stability reasons may be such to result in less than 50 SCB/1000TC, preferably less than 25 SCB/1000TC. By short chain branches in the present application are understood branches that may originate from a copolymerized co-monomer but also other way like, for example, short chain branches introduced by the catalyst via irregular ethylene incorporation. Increasing levels of short chain branches may improve Creep Life Time (CLT) properties of the yarns comprising the UHMWPE whereas the manufacturing of the gel-spun filaments may be negatively affected by too high levels of SCB. Creep Life Time may suitable be determined as described in W02018/060127 pages 11-13 and Figure 1 therein. In a preferred embodiment, the UHMWPE of the yarn comprises SCB being C1-C20-hydrocarbyl groups, preferably the C1-C20-hydrocarbyl group is selected from the group consisting of methyl, ethyl, propyl, butyl, pentyl, hexyl, octyl and cyclohexyl, isomers thereof and mixtures thereof as short chain branches. In the context of the present invention short chain branches are distinguished from long chain branches (LCB) that are herein defined as branches containing more than 20 carbon atoms but are often of substantially higher lengths reaching the dimensions of polymer chains themselves and resulting in a branched polymer architecture. Polymers having substantially no LCB are commonly referred to as linear polymers. Preferably, the UHMWPE is a linear polyethylene with less than 1 long chain branch (LCB) per 1000 total carbon atoms, and preferably less than 1 LCB per 5000 total carbon atoms.
In an aspect the gel-spun filament comprises an ultra-high molecular weight polyethylene (UHMWPE), wherein the UHMWPE has an intrinsic viscosity (IV) of at least 4 dL/g and comprises at least 0.3 short chain branches per thousand total carbon atoms.
The rope may be of various constructions, including laid, braided, parallel, and wire rope-like constructed ropes. The number of strands in the rope may also vary widely, but is generally at least 3 and preferably at most 16, to arrive at a combination of good performance and ease of manufacture. Preferably the rope is a braided rope, a laid rope, a parallel strand rope, a soutache braided rope or a parallel yarn rope. Most preferably the rope is a braided rope. In an aspect the rope is a soutache braided rope.
In one embodiment the rope is of a braided construction, to provide a robust and torque-balanced rope that retains its coherency during use. There is a variety of braid types known, each generally distinguished by the method that forms the rope. Suitable constructions include soutache braids, tubular braids, and flat braids.
The number of strands in a braided rope is preferably at least 3. There is no upper limit to the number of strands, although in practice ropes will generally have no more than 32 strands. Particularly suitable are ropes of an 8- or 12-strand braided construction. Such ropes provide a favorable combination of tenacity and resistance to bend fatigue, and can be made economically on relatively simple machines.
METHODS
• IV: the Intrinsic Viscosity for UHMWPE is determined according to ASTM D1601- 99(2004) at 135°C in decalin, with a dissolution time of 16 hours, with BHT(Butylated Hydroxy Toluene) as anti-oxidant in an amount of 2 g/l solution. IV is obtained by extrapolating the viscosity as measured at different concentrations to zero concentration.
• dtex: yarns' titer (dtex) is measured by weighing 100 meters of yarn. The dtex of the yarn is calculated by dividing the weight in milligrams by 10.
• Tensile properties of UHMWPE filaments:
Determination of filament linear density and mechanical properties carried out on a semiautomatic, microprocessor controlled tensile tester (Favimat, tester no. 37074, from Textechno Herbert Stein GmbH & Co. KG, Mbnchengladbach, Germany) which works according to the principle of constant rate of extension (DIN 51 221 , DIN 53 816, ISO 5079) with integrated measuring head for linear density measurement according to the vibroscopic testing principle using constant tensile force and gauge length and variable exciting frequency (ASTM D 1577). The Favimat tester is equipped with a 1200 cN balance, no. 14408989. The version number of the Favimat software: 3.2.0 .
Clamp slippage during filament tensile testing, preventing filament fracture, is eliminated by adaption of the Favimat clamps of the Favimat according to figure 1. The upper clamp 121 is attached to the load cell (not shown). The lower clamp 122 moves in downward direction (D) with selected tensile testing speed during the tensile test. The filament (125) to be tested, at each of the two clamps, is clamped between two jaw faces 123 (4x4x2 mm) made from Plexiglass® and wrapped three times over ceramic pins 124. Prior to tensile testing, the linear density of the filament length between the ceramic pins is determined vibroscopically.
Determination of filament linear density is carried out at a filament gauge length (F) of 50 mm (see figure 1), at a pretension of 2.50 cN/tex (using the expected filament linear density calculated from yarn linear density and number of filaments). Subsequently, the tensile test is performed at a test speed of the lower clamp of 25 mm/min with a pretension of 0.50 cN/tex, and the filament tenacity is calculated from the measured force at break and the vibroscopically determined filament linear density. The elongational strain is determined by using the whole filament length between the upper and lower plexiglass jaw faces at the defined pretension of 0.50 cN/tex. The beginning of the stress-strain curve shows generally some slackness and therefore the modulus is calculated as a chord modulus between two stress levels. The Chord Modulus between e.g., 10 and 15 cN/dtex is given by equation (A):
Chord Modulus between 10 and 15 cN/dtex = CM(10:15) 50 = - (N/tex) (A) e15 — e10 where: e10 = elongational strain at a stress of 10 cN/dtex (%); and e15 = elongational strain at a stress of 15 cN/dtex (%).
The measured elongation at break is corrected for slackness as given by equation (B):
EAB = EAB (measured)
Figure imgf000024_0001
where
EAB = the corrected elongation at break (%)
EAB (measured') = the measured elonoation at break (%) E5 = elongational strain at a stress of 5 cN/dtex (%) CM(5:10) = Chord Modulus between 5 and 10 cN/dtex (N/tex).
• Tensile properties of UHMWPE yarns: tensile strength (or tenacity) and tensile modulus (or modulus) of a yarn are defined and determined on multifilament yarns as specified in ASTM D885M (1995), using a nominal gauge length of the yarn of 500 mm, a crosshead speed of 50 %/min and Instron 2714 clamps, of type “Fibre Grip D5618C”. On the basis of the measured stress-strain curve the modulus is determined as the gradient between 0.3 and 1 % strain using a pretension of 0.2 cN/tex. For calculation of the modulus and strength, the tensile forces measured are divided by the titre, as determined above; values in GPa are calculated assuming a density of 0.97 g/cm3 for the UHMWPE.
• Short chain branches per 1000 total carbon (SCB/1000TC): is determined by NMR techniques and IR methods calibrated thereon. As an example the amount of methyl, ethyl or butyl short side chains are identical to the amounts of methyl side groups per thousand carbon atoms contained by the UHMWPE as determined by proton 1 H liquid-NMR, hereafter for simplicity NMR, as follows:
- 3 -5 mg of UHMWPE are added to an 800 mg 1 ,1',2,2'-tetracholoroethane-d2 (TCE) solution containing 0.04 mg 2,6-di-tert-butyl-paracresol (DBPC) per gram TCE. The purity of TCE is > 99.5 % and of DBPC > 99 %.
- The UHMWPE solution is placed in a standard 5 mm NMR tube which is then heated in an oven at a temperature between 140° - 150°C while agitating until the UHMWPE is dissolved.
- The NMR spectrum is recorded at 130°C, e.g., with a high field 400 MHz NMR spectrometer using an 5 mm inverse probe head and set up as follows: a sample spin rate of between 10 - 15 Hz, the observed nucleus -1 H, the lock nucleus - 2H, a pulse angle of 90°, a relaxation delay of 30 sec, the number of scans is set to 1000, a sweep width of 20 ppm, a digital resolution for the NMR spectrum of lower than 0.5, a total number of points in the acquired spectrum of 64k and a line broadening of 0.3 Hz.
- The recorded signal intensity (arbitrary units) vs. the chemical shift (ppm), hereafter spectrum 1 , is calibrated by setting the peak corresponding to TCE at 5.91 ppm.
- After calibration, the two peaks (doublet) of about equal intensity are used to determine the amount of methyl side groups are the highest in the ppm range between 0.8 and 0.9 ppm. The first peak should be positioned at about 0.85 ppm and the second at about 0.86 ppm.
- The deconvolution of the peaks is performed using a standard ACD software produced by ACD/Labs;
- The accurate determination of the areas A1 methyl side groups, hereafter A1 of the deconvoluted peaks used to determine the amount of methyl side groups, e.g., A1 = A1 first peak + A1 second peak is computed with the same software.
- The amounts of methyl side groups per thousand carbon atoms, is computed as follows: methyl side groups
Figure imgf000026_0001
- wherein A2 is the area of the three peaks of the methyl end groups which are the second highest in the ppm range between 0.8 and 0.9 and are located after the second peak of the methyl side groups towards increasing the ppm range and wherein A3 is the area of the peak given by the CH2 groups of the main UHMWPE chain, being the highest peak in the entire spectrum and located in the ppm range of between 1.2 and 1.4.
Figure 2 schematically represents a synthetic drive chain (400) comprising a strap (402), the strap comprising at least one layer containing a woven fabric and comprising holes (401). t is the length of a chain link and w is the width of a chain link. The chain may comprise a layered structure, wherein a plurality of layers containing a woven fabric are stacked and preferably attached to each other preferably by sewing, and wherein links are formed into the layered structure by cutting holes (401) along the structure in a preferably periodical fashion. The strap is having a length (Ls) much larger that its transversal dimensions of, e.g., width (w) and thickness (perpendicular to the plane of the drawing). Such straps can be readily made by weaving or knitting a multifilament yarns such as yarns made from high strength synthetic filaments as described herein into any construction known in the art such as a plain and/or a twill weave construction.
Figure 3a schematically shows an example of an embodiment of a monitoring device 100 for a synthetic lengthy body. Monitoring device 100 comprises a communication interface 150, a storage 140 and a processor system 130. For example, communication interface 150 may be configured to receive diffractive X-ray data, e.g., directly or indirectly from an X-ray sensor. Communication interface 150 may be configured to transmit data, e.g., an output of a machine learning model, e.g., a level of wear, or a warning signal, etc. For example, storage 140 may be configured to store X- ray input data, model output data, model parameters, and so on. For example, the processor system 130 may be configured to process diffractive X-ray data and/or to apply the model to it. Processor system 130 may be configured to execute computer instructions stored in storage 140. Device 100 may in addition or instead be configured to train the machine learnable model.
Storage 140 may comprise a local storage of device 100, e.g., a local hard drive or memory. Storage 140 may be non-local storage, e.g., cloud storage. In the latter case, storage 140 may be implemented as or comprise a storage interface to the nonlocal storage. Processor system 130 may comprise one or more local microprocessors. Processor system 130 may comprise one or more non-local processors, e.g., implemented in the cloud. Device 100 may be implemented as a system, the parts of which are distributed across different locations.
Device 100 may communicate with external storage, input devices, output devices, and/or with one or more sensors over a computer network. The computer network may be an internet, an intranet, a LAN, a WLAN, etc. The computer network may be the Internet. If device 100 is implemented as a system, the computer network may also be used for internal communication. The device may comprise a connection interface which is arranged to communicate within the device or outside of the device as needed. For example, the connection interface may comprise a connector, e.g., a wired connector, e.g., an Ethernet connector, an optical connector, etc., or a wireless connector, e.g., an antenna, e.g., a Wi-Fi, 4G or 5G antenna. Device 100 may comprise the X-ray sensor, or the X-ray sensor may be external to device 100.
The execution of device 100 may be implemented in a processor system, e.g., one or more processor circuits, e.g., microprocessors, examples of which are shown herein. Figures 3b-3d show functional units that may be functional units of the processor system. For example, the figures may be used as a blueprint of a possible functional organization of the processor system. Processor circuit(s) are not shown separate from the units in these figures. For example, the functional units shown in these figures may be wholly or partially implemented in computer instructions that are stored at device 100, e.g., in storage 140, e.g., an electronic memory of device 100, and are executable by a microprocessor of device 100. In hybrid embodiments, functional units are implemented partially in hardware, e.g., as coprocessors, e.g., mathematical, machine learning, e.g., neural network coprocessors, and partially in software stored and executed on device 100. Parameters of the network and/or training data may be stored locally at device 100 or may be stored in cloud storage.
Figure 3b schematically shows an example of an embodiment of a monitoring device 100 for a synthetic lengthy body. Embodiments of a monitoring device corresponding to figure 3b may be implemented on a device such as device 100 of figure 3a; embodiments may be implemented on a suitably configured computer, etc.
Figure 3b schematically shows a synthetic lengthy body 200 for monitoring by monitoring device 100. Monitoring device 100 may be employed during use of synthetic lengthy body 200; for example, monitoring device 100 may be incorporated in a lifting device or the like. This is not necessary, monitoring device 100 may also be used while the lengthy body 200 is currently not in use.
As set out herein, monitoring wear of a synthetic lengthy body is important for many types of synthetic lengthy bodies. With increased wear the risk of failure increases — failure which can be unacceptable for many types of load that may be entrusted to the lengthy body. A type of synthetic lengthy body for which monitoring is important is synthetics ropes. Although some examples below refer to ropes it is understood that wear management is also important for other types of synthetic lengthy bodies, such as synthetic chains, synthetics belts or a synthetic drive chains.
The source of the wear can vary. For example, wear may be caused by repeated bending over a sheave, or large and/or repeated tensile strain, etc. A particular problematic class of wear is wear which is internal to the synthetic lengthy body. For example, the wear may comprise one or both of internal melt, and/or internal abrasion. Probing internal wear, e.g., internal defects, is important: on the one hand, the majority of the material of synthetic lengthy body is internal, that is, inside the synthetic length body, yet visual inspection or other optical methods can only monitor external wear, e.g., defects visible on the outside of the lengthy body.
Accordingly, the wear in a synthetic lengthy body may be the accumulation of wear of various sources, e.g., internal melt, and/or, internal abrasion, and/or, repeated bending over a sheave, and/or, tensile strain, intra-filament wear, in particular, intrafilament wear due to bending.
External abrasion resistance is important in many rope applications. Concentrated damage due to rubbing against abrasive surfaces such as sheaves, fairleads, drums, flanges, winches, bollards and distributed damage due to dragging on deck or the ocean floor can both cause significant strength loss. Nevertheless, internal abrasion between filaments, yarns, and strands is one of the principal causes of rope degradation, especially in cyclic tension or bend-over-sheave service. The internal abrasion ultimately leads to intra-filament damage. In the present application, filament damage accumulation is detected using X-ray, with this technique changes in the crystalline domains are detected such as a change in the number of domains, in the size of the domains, in crystalline shape, distance between crystal planes, and I or in orientation of the crystal.
It was found that the structure and morphology of a synthetic lengthy body is altered with its use, e.g., with increased wear. Although, increased wear also causes changes in a non-synthetic rope, e.g., a wire rope, e.g., comprising iron, these changes have very different causes and express themselves in ways that are unrelated to the physics of synthetic rope.
For example, for a polyethylene lengthy body, e.g., a synthetic lengthy body comprising filaments made from polyethylene, in particular UHMWPE, increased wear causes a change in crystalline structure and/or morphology. For example, for an aramid lengthy body, e.g., a synthetic lengthy body comprising filaments made from aramid, the crystalline/amorphous structure and/or morphology in the synthetic lengthy body changes with wear. These internal changes in the synthetic lengthy body cause changes in the measurements that may be obtained with diffractive X-ray sensing. Unfortunately, the relationship between level of wear and the particular change in the synthetic lengthy body is not straightforward.
With wear, the intra-filament alignment in a synthetic lengthy body may also change, e.g., with a decreasing organization; likewise for other filament damage, e.g., broken filaments. Better aligned fibers have better mechanical properties. Alignment can be lost locally, e.g., because of internal abrasion, e.g., because it cuts the fibers. Alignment loss can be derived from the diffractive X-ray data. Interestingly, like crystalline structure changes the relationship between a level of filament alignment and level of wear is not straightforward. Analysis becomes more complex when filament alignment is taken as an indicator of level of wear together with one more of these other indicators, e.g., crystalline changes. Using a machine learning model makes it possible to incorporate these disparate level of wear indicators. For example, intra-filament alignment decreases during internal melts, which may occur in use, and which cause wear.
Intra-filament molecular alignment, e.g., of PE molecules or PE crystals, can be measured from the 2D x-ray data, in particular before radial integration. Generally speaking, the x-ray scattering pattern will be more anisotropic the more oriented the filaments are. For example, one may integrate radially instead of not azimuthally, to reduce the dimensionality of the data, e.g., from 2D to 1 D. For example, the radially integrated data may be offered as an additional input of the model. For example, a machine learnable model receives as input both radially integrated X-ray data and azimuthally integrated X-ray data. Having two different types of integration at the input of a machine learnable model still significantly reduces the amount of input data, e.g., compared to the 2D un-integrated data, and thus, reduces the number of parameters and training time; yet having both types of integration allows the model more relevant information that is correlated with wear, thus increasing model accuracy at only a modest increase in the amount of input data. Integrating the data, both radially and azimuthally has the further advantage that it reduces the chance of the model training focusing on arbitrary artefacts that happen to be in the particular training data used.
Shown in figure 3b is an X-ray sensor 110 and an X-ray source 111. X-ray sensor 110, X-ray source 111 and synthetic lengthy body 200 are shown in figure 3b external to device 100. Device 100 may be configured to obtain diffractive X-ray data 112 from sensor 110, e.g., through a communication interface, e.g., a sensor interface. For example, X-ray sensor 110, X-ray source 111 and synthetic lengthy body 200 may be incorporated in another device, e.g., a lifting device. On the other hand, X-ray sensor 110 and X-ray source 111 may also be part of device 100. The latter is of advantage in an installation where, say, a rope is monitored while in use. Associated with X-ray sensor
110 and source 111 may be spacers, etc., for aligning and spacing the synthetic lengthy body.
X-ray source 111 is configured to direct X-ray radiation to at least part of the synthetic lengthy body 200. X-ray sensor 110 is configured to sense diffracted X-ray radiation coming from lengthy body 200. The diffractive X-ray data 112 corresponds to the synthetic lengthy body 200 and has been obtained therefrom. The diffractive X-ray data may indicate a diffraction image. For example, X-Ray sensor 110 and X-ray source
111 may be configured for obtaining diffractive X-ray data for the lengthy body 200. For example, the diffractive X-ray data may be obtained from a single direction, and/or with a single X-ray wavelength. Multiple directions and/or wavelengths may be used to improve the information obtained from the lengthy body. It is possible to obtain multiple sets of diffractive X-ray data, e.g., multiple X-ray diffraction images, even from the same location in the rope, e.g., with different direction/angle or wavelength. Multiple sets of diffractive X-ray data may be incorporated in the machine learning model.
An advantage of using diffractive X-ray is that internal wear indicators such as those mentioned above cause a change in the diffractive X-ray data and thus can be used to determine a level of wear therefrom.
The X-ray radiation emitted from source 111 may have various photon energies. Experiments at lower and at higher photon energies have both shown that level of wear can be determined. An advantage of lower photon energies is that the installation can be smaller and more compact. An advantage of higher photon energies is that the data that is obtained may comprise more information and may penetrate through thicker ropes. Moreover, as X-ray absorption in the rope goes down with higher energy it is easier to monitor thicker ropes at higher energy levels. Experiment showed that it was easier to get high reliability using higher photon energies. On the other hand, at lower energy useful predictions of level of wear can be made; as discussed below there are various strategies to increase the signal-to-noise, also for lower energy levels.
In an embodiment, the photon energy may be from at least 5 keV, for example, the X-ray radiation having a photon energy in a range from 5 keV up to 400 keV. Experiments were performed at both a low and a high energy level. In practice, X- ray sources are easily available well over 100 keV, e.g., at 160 keV, 230 keV. Using, e.g., an alloy source the energy level may be varied. Useful ranges include, but are not limited to, e.g.,
- from 5 keV up to 35 keV, e.g., of 8 keV; this range has the advantage that in this range most of the useful lines emerge. For example, copper may use an x-ray energy of 8 keV, 17 keV for Molybdenum, etc. At the same time energy levels remain low,
- from at least 50 keV, e.g., in the range from 50 keV up to 100 keV, e.g., of 88 keV. This higher energy range gives a similar pattern in the diffractive X-ray data. However, one can penetrate deeper in the material, e.g., go through thicker materials. Moreover, polyethene material does not absorb as much X-ray energy at higher keV levels. An intermediate position between 35 keV and 50 is also possible. Upwards of 100 keV may also be used, e.g., from 100 keV up to 230 keV; or from 100 keV up to 400 keV, etc. For example, one may use synchrotron-based high- energy x-ray diffraction. A synchrotron may be used to obtain high energy x-ray radiation.
Depending on the energy the scattering geometry has to be chosen, so that relevant crystal lattice plane distances (d-spacings) can be measured. To this end X-ray energy E can be converted to X-ray wavelength A by the known relationship of the 1239 842 product of speed of light and Planck’s constant via 2 = - E : — eV nm. The d-spacing (d) is defined by the following equation:
Figure imgf000032_0001
where Q is the magnitude of the scattering vector and 29 is the scattering angle, i.e., the smallest angle between the incident beam and the scattered beam. As known in the arts, depending on the scattering geometry a minimum and maximum d-spacing can be measured, relating to a maximum and minimum measurable scattering angle, respectively. The maximum measurable scattering angle is related to the maximum distance between the two points where the scattered and the incident (unscattered) beams intersect with the detector surface (a technical implementation of a 1 D detector, e.g., line detector, has a surface too) and the distance from the sample to said detector by a simple trigonometric relationship, given that the incident beam is parallel to the normal vector of the detector. In practice the maximum d-spacing is limited by the smallest scattering angle that can be measured, which is limited by the size of the beamstop and the sample-to-detector distance. The minimum d-spacing obtainable is governed again by the sample-to-detector distance and the maximum distance between the intersects of the scattered and unscattered beam with the detector. Values for the detector size and sample-to-detector distance should be chosen so that the relevant crystal lattice plane distances can be measured. For example for PE, the most important d-spacings with respect to this invention lie between 0.35 and 0.50 nm. Since the proposed machine learning algorithm can select the most relevant d-spacing on its own, it may be beneficial to increase the measured range to fall within 0.25 and 0.6nm, more preferably between 0.15 and 0.7 nm and even more preferably within 0.1 and 1 nm. As a general algorithm to define the optimal d-spacing range for any polymer material can be described as follows: obtain the scattering intensity as a function of d-spacing within the range of 0.15 and 0.7nm, which for most polymers used for synthetic lengthy bodies is known in the literature and select highest intensity peak, i.e., the most prominent peak. The lower bound of the desired d-spacing range will be obtained by subtracting the delta of 0.10 nm, the upper bound by adding the delta of 0.10 nm to the d-spacing of the most prominent peak. For example for PE, the most prominent peak would be approximately at a d-spacing of 0.41 nm (corresponds to Q = 15 nm 1, see Figure 10f). The resulting d- spacing range to be measured would thus lie between 0.31 and 0.51 nm. As explained above, preferably the added or subtracted delta is expanded and is 0.15 nm, even more preferably 0.2 nm and most preferably 0.5 nm. The subtracted delta shall not be larger than the d-spacing of the most prominent peak minus 0.05 nm, so that the resulting range is always positively defined.
In an embodiment, the X-ray source 111 is point focused, e.g., where the X- ray beam from the source has a circular cross-section. This is a typical choice, as recorded data from the point-focused source is more easily interpretable. It is also possible to use different primary beam cross-sections. It is also possible to use a line- focused source, which increases the X-ray flux compared to the point-focused source. Various orientations of the line with respect to the rope are possible, e.g., in an embodiment, and orientation parallel to the synthetic lengthy body is used. Other orientation could also be used though. For example, the line-focused source from a Kratky-camera system may be used. In the art, line-focus is not advised for oriented systems, as data becomes convoluted over the line. Following this belief, a line-focused X-ray source should not be applied to a rope, since it is an oriented and non-homogenous system. Conventionally, the data obtained from the line-focused X-ray source would be very hard to interpret. However, for a machine learning model deconvolution is not needed to make the classification. Since the diffractive X-ray data is correlated to wear, the machine learning model can interpret the data using this correlation — ease of human interpretation is not a factor for a machine learning model. An advantage of line-focus is that a higher flux can be more easily obtained, e.g., less time needed to record one set of X-ray data. In an embodiment, the X-ray source is line-focused.
In an embodiment, the X-ray source 111 and sensor 110 may be installed in a load lifting device, e.g., in a crane or the like. Figure 4 schematically shows an example of an embodiment of a load lifting device 800, in this case a crane. Load lifting device 800 comprises a synthetic lengthy body 820, e.g., a rope. Load lifting device 800 is arranged to lift a load 830 with the synthetic lengthy body 820. Installed in load lifting device 800 is a monitoring device 810, e.g., such as monitoring device 100, configured for the synthetic lengthy body. Instead of the full monitoring device, the monitoring device may be distributed over multiple locations, e.g., X-ray source and X-ray sensor may be installed in lifting device 800, while the machine learnable model may be running on a computer located elsewhere. There may be further elements in figure 4 than shown, e.g., sheaves, drums, etc.
The advantage of obtaining diffractive X-ray data in lifting device 800 is that the rope does not need to be decommissioned in order to obtain information on the level of wear of the rope. For example, in an embodiment, shadowing may occur. For example, in an embodiment, about half of the X-ray beam may be shadowed and the nonshadowed part is measured. This installation can be made compact for installing on a crane, e.g., adapting the X-ray optics. Even with shadowing one can still get enough data due to symmetry of the scattering pattern. The data is centrosymmetric, so even with shadowing part of the rope, does not lose information, at most intensity. Accordingly, an embodiment can be used even in an installation where the measuring geometry does not allow one to scan the whole synthetic lengthy body.
If the beam hits an area where there is less material on the right of the beam than on the left of the beam, the scattered beam can still be transmitted through the rope side with less material, though perhaps, not on the thicker one. Accordingly, the shadowed data can be used. Data quality may be improved by modifying the geometry of detection in this case; For example, having two detectors to extend the recording of the data on the side where there is no shadowing. In an embodiment, the detector is moved in case of shadowing. This may be done automatically, for example, shadowing may be detected from the X-ray data, from which in turn a detector move signal may be generated to effect a movement of the detector to capture more of the non-shadowed data.
As another example, a monitoring device such as monitoring device 100 or 810 may be configured for under water use. For example, one or more spacers may be arranged for spacing the lengthy body form the X-ray source and X-ray sensor while the lengthy body is submerged in water during operation of the X-ray source and X-ray sensor.
For thinner ropes, the quality of diffractive X-ray data obtained at a given wavelength increases. For example, excellent data may be obtained for a rope thickness of 2 mm, e.g., measured as the largest diameter of the rope in the direction of the x-ray radiation. Rope samples may be analyzed of very low thickness, e.g., having a diameter as low as 20 micron, such as having a diameter in the range from 20 to 100 micron.
Thicker rope is possible but the quality of the diffraction data may be reduced. Interestingly, with machine learning models various approaches may be developed for estimating a level of wear even for thicker ropes. Even without additional strategies to increase signal to noise, ropes as having a 1 - 2 centimeter diameter can be monitored.
For thicker ropes, one may analyze the rope at multiple positions and aggregate the results. Even thicker ropes may be analyzed, e.g., ropes of up to about 88 mm or larger. Rope thickness need not be a limiting factor in diffractive X-ray monitoring, as one or more of the measures described below or elsewhere herein can be used.
For example, thicker ropes can be probed by increasing X-ray energy, e.g., using a high-energy X-ray source, e.g., a synchrotron or any of the other options available in the art.
For example, machine learning turns out to be particular good solution for analyzing the multiple scattering coming from the thicker ropes. The relationship between scattering and wear is hard even for thin ropes, but machine learning can quantify this relationship both for thin and thick ropes, and at low and high energy levels. It was found to be one of the advantages of applying machine learning that the relationship which becomes increasingly hard with increasing rope thickness can still be tackled.
For example, multiple measurements may be taken to improve the data. Multiple measurements can be taken, for example, at different positions in the same rope. Multiple measurements can be taken at different settings, e.g., direction or energy level. Multiple measurements can be aggregated or may be offered to as multiple inputs to a single model. Additional inputs to the model may also or instead come from other sensor modalities than diffractive X-ray, and can be for example as simple as diameter of the lengthy body.
Yet a further way to deal with thicker ropes, in particular, ropes having a diameter, such that the X-ray radiation does not go through the full diameter of the rope, e.g., rope having a diameter of > 3 cm, is to aim the x-ray on a strand in the rope, or to fully or partially disassemble the rope. The latter allows for easier aiming towards a single strand and this to obtain better x-ray data. For example, figure 10b shows the disassembling of a rope. For example, X-ray data may be obtained by directing X-ray radiation to at least part of a strand isolated from the part of the lengthy body. Using machine learning models, with or without the means mentioned above to improve the signal-to-noise ratio in the X-ray data, synthetic lengthy bodies, e.g., ropes, can be successfully monitored for wear having any diameter.
Figures 5a-5d further illustrates one of the techniques to improve monitoring a thicker rope. Figure 5a schematically shows an example of an embodiment of a monitoring system for a synthetic lengthy body, in a side view. Figure 5b schematically shows the same embodiment but in a top view. Shown in figure 5a is an X-ray source 5, a lengthy synthetic body 4, in this case a rope 4, and an X-ray sensor. Instead of a rope, this embodiment could also be implemented with another type of lengthy body, say a synthetic chain.
The X-ray source 5 is configured to direct X-ray radiation 1 to at least part of rope 4. As shown, the rope is not disassembled, though it could be. An X-ray sensor 6 is arranged to obtain diffractive X-ray data corresponding to rope 4. Source 5 and sensor 4 are arranged to scan lengthy body 4 to obtain diffraction data, e.g., a diffraction image. Sensor 6 is configured to measure the diffractive X-ray parts 2 and 3. A beam stop 8 is schematically indicated on sensor 6 to block the non-diffractive part of the X-ray radiation. A digital mask may be applied to the measured X-ray data, e.g., to crop the area of the beamstop plus its immediate vicinity or areas where dead pixels are, etc. The scattering angle, e.g., the smallest angle between the incident beam and the scattered beam, has been indicated in figure 5a as ‘20’, not to be confused with diffractive X-ray part 2.
The diffractive X-ray data is sent to a computer 7 which is configured according to an embodiment. In particular, computer 7 may be configured to apply a machine learnable model to the diffractive X-ray data, in some processed form. For example, computer 7 may be configured to generate a warning signal if a level of wear obtained from machine learnable model’s output exceeds a wear threshold. Figures 5a and 5b show a particular orientation of the synthetic lengthy body, but the orientation may be different.
Figure 5c schematically shows a first example of a detail of an embodiment of a monitoring system for a synthetic lengthy body, in a top view. In the configuration of this embodiment shown in figure 5c, the X-ray radiation is directed, e.g., aimed, at the center of lengthy body 4. Figure 5d schematically shows a second example of a detail of an embodiment of a monitoring system for a synthetic lengthy body, in a top view. In the configuration of this embodiment shown in figure 5c, the X-ray radiation is directed tangentially to the lengthy body. Figure 5d also shows an example of directing the X-ray radiation towards a strand of the lengthy body, in this case, without isolating the strand. An advantage of the configuration of figure 5d is that the X-ray radiation passes through less of the synthetic lengthy body; accordingly, a higher signal-to-noise ratio is obtained. To compensate for the loss in bulk information by illuminating only part of the rope, the rope or the X-ray can be rotated and diffracted X-ray pattern accumulated from multiple angles. Interestingly, data quality is improved while still allowing to analyze a thicker rope.
For example, one may focus an X-ray beam of source 5 on a side of the rope. For example, the side may have a thickness of about 2 mm, in a range from 1 mm up to 3 mm. However, with increased X-ray energy, increased thicknesses of the sides become possible. For example, the side thickness may be measured as the distance passed through the rope in the direction of a center line of the beam. Note that full control over the side of the synthetic lengthy body through which the X-ray beam passes is possible by precisely focusing the X-ray beam; note that focusing can be done down to the nanometer scale if needed. Focusing may be used to define the illuminated area on the rope. Seeking the shadowing position can be achieved by translation of the rope in reference to the beam. This could also be done by focusing the x-ray beam onto another location, e.g., closer to thicker area.
An advantage of obtaining diffractive X-ray data tangentially, e.g., from a side, is that high-quality X-ray data is obtained without having to disassemble the rope. This makes this approach especially advantageous to apply in a lifting device, e.g., a crane. For example, the level of wear in a rope used in the lifting device can be estimated without having to remove the rope from the lifting device.
Tangential direction of X-ray radiation may use a point-focused X-ray source, or a line-focused X-ray source. For example, the X-ray may be focused along the rope, e.g., in a slit-format. This reduces measurement time. Analyzing such data with conventional tools is hard, but interpretation can be done with a machine learning model.
Figure 6a schematically shows an embodiment of a synthetic multi-strand rope 500, in this case a braided 12 strand rope (12x1), which is an example of a synthetic lengthy body. The braided strand rope has a braiding pitch I, a length dimension L (depicted along the x-axis) and transverse dimensions Tz and Ty (along the z and y- axis). The transverse dimensions of a rope are herein also referred to as width and thickness of a rope. For a circular rope, that is a rope having a round diameter, Tz and Ty are substantially the same and typically referred to a diameter of the rope. Such rope would herein have a thickness of Tz. The braided rope 500 comprises multiple strands, three of which are shown with a reference numeral: strands 501 , 502, and 503. Strands 501 , 502 and 503 have strand direction indicated as a solid line. In a synthetic lengthy body comprising multiple strands, in particular in braided ropes, two strands can be parallel or crossing. Strand 501 and 502 are crossing strands. Strands 501 and 503 are parallel strands. Between strands there is a contact area. Shown in figure 6a is a crossing zone 510 between two crossing strands, and a parallel zone 511 between two parallel strands. Although an X-ray beam could be focused anywhere on the rope, improved results are obtained when the X-ray beam is directed towards a contact zone between two strands.
Figure 6a shows a first example of directing an X-ray beam. X-ray beam 520 is directed towards, e.g., focused on, a zone 510 where two strands cross; in this case strands 501 and 502. Typically, X-ray beam 520 will be perpendicular to zone 510. Alternatively, the X-ray beam could be pointed at the parallel zone 511. Preferably the X-ray beam 520 is pointed at an area of crossing strands, e.g., zone 510 as this is deemed very effective.
Figure 6a shows a second example of directing an X-ray beam. The X-ray beam 521 is directed to the edge, e.g., the side of the rope, e.g., at a side zone. For example, the X-ray beam may be directed perpendicularly through the rope. For example, beam and rope may make a straight angle. Preferably, X-ray beam 521 is pointed perpendicular, e.g., along the z -axis, to the length dimension L of the rope to a point located in de rim 530 of the rope. In an aspect the X-ray radiation travels through ca 3 mm of rope material.
For ropes that are thin relative to the energy level of the X-ray beam, aiming the X-ray radiation to a crossing zone, i.e., a zone 510 where two strands cross, is preferred, as it is expected to be the most effective.
Figure 6b schematically shows directing an X-ray beam to the rim of a synthetic lengthy body. The figure schematically depicts a cross section of a lengthy body 700, in this case a braided 12 strand rope (12x1). The cross section schematically shown in figure 6b corresponds to the rope schematically shown in figure 6a. The braided rope 700 comprises a strand 701 . An X-ray beam 730 is pointed perpendicular (along the z - axis) to the length dimension of the rope and measured in the rim of the rope. In an aspect the X-ray radiation travels through ca 3 mm of rope material. For example, the X- ray beam may be directed through 2 mm to 4 mm of rope material. Figure 7 schematically depicts a lengthy body 60, in this case a chain comprising chain links. Two chain links have a reference numeral in figure 7: chain link
61 and chain link 64. The X-ray beam (not shown in figure 7) may be pointed at the synthetic material of the chain. For example, the X-ray beam may be directed at zone
62 of chain link 61 or at zone 63 of chain link 64. Preferably, the beam is pointed at the contact location 62 through which loads are directly transmitted between said chain links.
Figure 8 schematically depicts a lengthy body 90, in this case a laid rope comprising strands 91 having an outer surface 92 on the outside of the rope. The X-ray beam 94 may be pointed at rope 90. For example, X-ray beam 94 may be pointed at a strand contact area where two strands contact each other; for example, in an area indicated with zone 93. The X-ray beam may be pointed perpendicular (along the z - axis) to the length dimension L of the rope to a point located in de rim (95) of the rope. In an aspect through ca 3 mm of rope, e.g., between 1 mm and 4 mm. The length of rope through which the X-ray beam passes depends on energy in the X-ray beam.
Returning to figure 3b. Monitoring device 100 comprises a machine learnable model 160 that is provided in the device. For example, device 100 may comprise a storage, e.g., a memory comprising trained parameters of model 160. For example, the machine learnable model may be associated with multiple parameters that determine how an output value is obtained from the input values. The multiple parameters may be adapted in a training process so that the machine learnable model learns to approximate data for which the relationship between sensor data and a level of wear is known. Figure 10g which is further discussed below provides an example of model parameters; the figure shows in the form of a graph the parameters of an LDA model.
Shown in figure 3b is that machine learnable model 160 produces a quantifying value 161 that indicates a level of wear. Many types of machine learnable model may be used. For example, regressive ML models may be used, which learn to approximate a level of wearvalue from the input values. For example, a regressive model may produce a quantifying value that an amount of lifting, e.g., in weight, in time, or in weight and time, lengthy body 200 has yet endured.
For example, the machine learnable model may be a classification model, e.g., trained on classifying the input sensor data in a level of wear class. As the predicted class is indicated by one or more numerical values, the classification model may be used to predict a level of wear as well. For example, an output that indicates likelihood that a rope is broken, may be used as an indication of a level of wear. In an example, there may be two level of wear classes, e.g., new versus broken; in an example, there are three level of wear classes, e.g., low wear, medium wear, and high wear. Thus, the quantifying value may be a numerical value that indicates a particular wear class.
It was found that diffractive X-ray data relates to a level of wear information, e.g., because the crystalline/amorphous structure in a synthetic rope changes with use, which in turn affects the diffractive X-ray data. Unfortunately, the relationship between diffractive X-ray data and the condition of the rope is non-linear and non-obvious. In particular, the state of the art does not provide quantitative models linking the crystalline morphology to the mechanical properties of the rope. It was found that this correlation can be made explicit in a practical method using a machine learning model.
Further complicating analytic models is that large amounts of data may need to be collected. This holds for training, but also for application. Data may be collected over a very long rope. Moreover, data quality is limited due to the fact that it may be collected in the field instead of in a well-defined laboratory. Also, the measurement time per point may be short, e.g., to limit the collection time, which further limits data quality. In training, data is necessarily collected from ropes that differ from the rope on which the model is used in practice. Furthermore, multiple scattering may have to be incorporated if one probes thick ropes. All this is problematic with standard crystallographic analysis, but was found to be solvable with a machine learning approach.
In an embodiment, the diffractive X-ray data is processed before inputting it to the machine learnable model. Processing may be relatively minimal, e.g., collecting multiple sensor data, applying a filter, or the like. Such approaches can be used for example, when applying ML models with a high number of parameters, and when using a large amount of training data. An example of such a model is a neural network.
An advantage of applying a dimension reduction algorithm is that the number of parameters in the trained model can be reduced. Accordingly, fewer samples are needed fortraining the model. It was found that pre-processing the data can also improve the machine learning. For example, a potentially problematic aspect of scanning using diffractive X-ray data is the large number of sensor values, due to the high dimensionality of the data. An advantage of concentrating the available information into fewer data items may be that the model will better learn to generalize.
Furthermore, dimension reduction avoids potential problems with aligning the rope. Tilting the rope will tilt the X-ray data, but by performing the dimension reduction the tilting may be removed. A dimension reduction algorithm is not required. For example, a large parameter model such as neural network may be trained on diffraction data without dimension reduction. This has an advantage since the construction of the rope is also affected by the wear. Construction changes will be better visible in the unreduced diffraction data.
Typically, dimension reduction is from two to one dimensions. However, in an embodiment, the X-ray source, and sensor may cooperate to produce a 3-dimensional X-ray diffraction tomography which records a spatially-resolved X-ray diffraction image of the synthetic lengthy body. In such a case, dimension reduction may be from 3 to 2 dimension or from 3 to 1 dimensions. For example, in an embodiment, the dimension reduction algorithm comprises azimuthally integrating. This is an example of reducing from 2 dimension to 1 dimension.
The diffractive x-ray data may be pre-processed by computing particular values. For example, these may be values that are known how to compute analytically, and are known to be relevant but particular relationship between those values and a level of wear may nevertheless be unknown. For example, in an embodiment, the preprocessing may comprise calculating a fraction in the part of the lengthy body in a specific crystalline phase. In that case, the machine learnable model may be applied to calculated values. The evolution of morphology during mechanical wear, as a physical phenomenon, is non-linear in nature. For instance, the evolution of crystallite sizes, content of each crystal phase and evolution of the orthorhombic unit cell dimension have been shown to be nonlinear with respect to mechanical wear.
For example, in a synthetic lengthy body comprised of a PE material, such as UHMWPE high strength synthetic filaments, pre-processing may comprise calculating a fraction in the part of the lengthy body in monoclinic crystalline phase, orthorhombic crystalline phase, and/or overall crystallinity. The machine learnable model may be applied at least to these calculated fractions.
It is also possible to use hybrids. For example, the machine learnable model may receive as input one or more computed values as well as dimensional data. For example, a neural network may receive 1 or 2 dimensional input as well as computed crystalline phase fractions.
Many types of machine learnable model may be used. Examples are included herein. The machine learnable model may be configured to receive the processed diffractive X-ray data and to generate a value quantifying a level of wear in the part of the lengthy body. The predictive accuracy of the machine learnable models was found to be surprisingly high, given the inherent complexity of the problem. For example, even using low-energy diffractive data combined with a geometry with small d-spacing range, which comprises relatively little information and more noise compared to high-energy diffractive data and a geometry with large d-spacing range a predictive accuracy of over 90% can be obtained (in this experiment, the model classified input data into high versus medium or low wear). When the model is tasked to predict intermediate wear as well, still a predictive accuracy of over 70% is obtained. Both values have been improved, e.g., by taking additional data into account for a prediction. For high-energy samples, the results are even better, achieving even a 100% predictive score on classification tasks.
Monitoring device 100 may comprise a post-processing device 170. For example, post-processing device 170 may be configured to generate a warning in dependence on the generated value. For example, device 170 may comprise a display or a speaker to display or to sound a warning, or show the value, and so on. Device 170 may be configured to derive other measures from the value generated by the trained model, e.g., to go from level of wear to estimated remaining lifetime, etc.
The level of wear predicted by the model may interpolate between known wear classes. For example, the level of wear may correlate with various wear indicators; For example, the level of wear may correlate to and/or be trained on residual break strength, number of equivalent rope cycles.
For example, wear may be expressed as the number of equivalent cycles in a controlled wear experiment. For example, the model may be trained to predict the number of wear cycles a rope experienced in training. Even if the actual wear in use is different the resulting prediction may be used as a level of wear indication.
In practice, the level of wear obtained from the model may be used as a discard criterion. For example, a warning signal may be generated if the level of wear indicated by the value exceeds a wear threshold. For example, the signal may be displayed on a display, or a sound may be generated, etc. For example, the level of wear may indicate at what wear level the rope needs to be discarded. For example, if a level of wear exceeds a threshold, then this may be used as a signal to discard the rope. The level of wear generated by the model may be used in combination with other indicators of wear. For example, a number of cycles or an amount of time the rope has been used may be used a further indicator. When the number of cycles is low, a high level of wear prediction of the model is needed for discarding; when the number of cycles is higher, a lower level of wear prediction of the model is needed for discarding. The unit in which the level of wear is expressed may be determined by the data on which the model is trained. For example, a model may be trained on data bins with low, medium, and high wear, as a result of which the model may output values low, medium, and high wear. For example, the model may interpolate between low, medium, and high wear. The particular threshold depends on the application, e.g., on the risk that can be taken.
In an embodiment, an estimated remaining lifetime may be derived from the generated value, e.g., from the level of wear. The estimated remaining lifetime may be expressed in a similar metric as the level of wear. For example, in an estimated number remaining predefined cycles. The actual remaining lifetime will depend on the future rate of degeneration, which might not be known. Remaining cycles may be used as a proxy for remaining lifetime. For example, a look-up table or a function, etc., may translate level of wear into estimated lifetime. A remaining lifetime estimator may be built up using a nearest neighbor algorithm on further training data. For example, a training set may be obtained comprising pairs of level of wear as predicted by the model, and actual remaining lifetime. A further machine learning model may map the former to the latter, e.g., the nearest neighbor algorithm.
In an embodiment, the monitoring device, e.g., the machine learnable model, is configured to compute a confidence level for the level of wear or, in case of multiple samples, an aggregated level of wear. For example, the device may be configured to compute multiple values, e.g., multiple levels of wear, e.g., from multiple samples of the same rope. The average of those multiple levels of wear may be taken as the aggregated level of wear, and a standard deviation of the multiple levels of wear as a confidence value. Note that wear is not a point phenomenon, but occurs over an extended length. Accordingly, it is expected that multiple values will be similar, so that taking them in to account together increased accuracy. Multiple measurement may be taken at a distance along the rope, but may especially also be taken by rotation of the rope, e.g., measuring X-ray under multiple angles at the same or about the same length along the lengthy body. The latter was found to be especially suited for combining, e.g., averaging, multiple levels of wear indications.
For example, in an embodiment, a user can configure the length along the body over averaging is done, e.g., is permissible. A suitable range may depend on the application of the rope, so that in some application wear may vary stronger over shorter rope lengths than in other application. For example, a map may be generated indicating that wear in different parts of the rope, e.g., to enable the skilled person when taking a decision on partial rope replacement.
In an embodiment, a confidence value may indicate how similar the current input data is to input data seen during training. If the input data conforms to a data distribution known from training, then it is likely that a good value will be computed for it. Known machine learning methods are available to estimate how close a new input is to data seen during training. If the input data does not conform to a data distribution known from training, then the model will be able to compute a value for it, but it is unlikely that it is realistic. In an embodiment, a warning signal is generated if the current input data does not conform sufficiently to the training data, e.g., within a predetermined bound. The system may be configured to refuse to output an unrealistic value in such a case.
A confidence value may be used as an additional discard criterion. For example, if confidence is high and level of wear is high, e.g., over some thresholds, then the rope may be discarded. But if confidence is low, other indicators may be used, e.g., known history of the rope, e.g., number of previous load cycles etc.
Another application of a confidence value is to use it as an indicator to obtain additional data. For example, if confidence is low then additional diffraction X-ray data may be obtained and analyzed. For example, the device may be configured to generate a warning signal if the confidence level is below a confidence threshold and/or obtaining further X-ray data and recomputing the level of wear or aggregated level of wear. Additional data may be obtained from a different part of the synthetic lengthy body, but may also refer to another rotated position or different tangential position if the measurement was not centered on the rope, e.g., on the other side of the rope. If the measurement was centered on the rope, then an additional measurement may measure tangentially.
Predictions can be aggregated, e.g., by taking the worst level of wear from multiple levels of wear indications. Multiple level of wear values may be combined, say, by discarding the best and worst value, and averaging the rest. If data is aggregated, the model may generate a level of wear and a confidence value for each of the multiple inputs, e.g., multiple diffractive X-ray data. The multiple levels of wear may be combined into one aggregated level of wear, while the multiple confidence values may be combined into one aggregated confidence. For example, the aggregated level of wear may be a weighted average, with the weights derived from the confidences, e.g., equal to them.
In an embodiment, the following formula is used for the confidences.
Figure imgf000045_0001
In the above formula, C represents the class, e.g., in this particular example these may be the classes NBZ, SBZ, DBZ; pc l represents the normalized joint probability for class C and measurement number (i); pk l represents a probability estimate for a measurement number (i) and a class (k), e.g., in this example these may be the classes NBZ, SBZ, DBZ; N represents the total number of measurements; i represents the measurement number, in this case running from 1 to N. For example, each number of measurements may correspond to one level of wear prediction. The above example provides an advantageous choice to compute confidences but other formulas may be used as well.
For example, wear level and confidence may be represented as tuples, where wear levels are fixed levels and confidences float. Summing all confidences for all tuples, e.g., for all possible wear levels, gives 1. Wear level needs to be in one of the cases. The wear level of a single measurement is selected by selecting the corresponding highest confidence. The aggregated wear level is selected as the wear level that has the highest aggregated confidence; however, the wear level is still one of the original levels. Further examples are given herein.
For example, if confidences are expressed as a standard deviation, then one possible choice for the weights is given by the reciprocal of variance. A new confidence for the combined value may be computed from the standard deviations.
In an embodiment, the monitoring method is a synthetic lengthy body health and/or synthetic lengthy body condition monitoring method. For example, the level of wear may be monitored periodically or even continuously. A graph of level of wear and/or confidence may be produced. A warning may be generated if the graph shows changes, e.g., increase or decrease over a threshold, etc.
In an embodiment, multiple diffractive X-ray data by rotating the synthetic lengthy body and/or by taking X-rays under multiple angles. For example, the X-ray sensor may be associated with a mechanism arranged to measure X-ray data in tangential fashion at different points radially along the rope, e.g., from the left part of the rope and from the right part, or at more than two points. The machine learning model may be configured to receive the multiple data, or the multiple data may be provided to the model separately after which the results may be combined.
In an embodiment, monitoring device 100 may comprise a recognition unit 120. Recognition unit 120 may be configured to apply a recognition algorithm on the obtained sensor data. The obtained sensor data may be discarded if the obtained sensor data does not correspond to valid diffractive X-ray data of a synthetic lengthy body. Recognition unit 120 is optional. For example, if the environment wherein the diffraction data is obtained is sufficiently under control, then recognition unit 120 is not necessary. However, it can be an advantage to discard bad diffraction data. It can happen that the sensor data received from the sensor is not correct data. For example, the X-ray source may be incorrectly aimed at the rope, or may be aimed at a different object. For example, if tangential aiming is used, it may happen that the x-ray source is aimed too close to the center of the rope, in which case, too much X-ray radiation will be absorbed, and the data reduces too much in resolution, or that the X-ray source may be aimed too much towards the edge of the rope, in which case too much of the X-ray radiation misses the rope altogether. In either or in both cases the rope recognition algorithm may discard the data and/or warn against the bad data.
Furthermore, the rope recognition algorithm may verify that the synthetic rope is of the correct type. For example, if PE rope, e.g., a Dyneema rope is expected, the rope recognition algorithm can verify that the correct type rope is used. This can be important since the trained model is typically trained for a rope with a particular composition. For example, in an embodiment of the rope recognition algorithm, the intensity of the received sensor data can be computed. If the intensity is below a threshold a warning or discarding may be triggered. For example, the rope recognition algorithm can compute a discrepancy between a location of peaks in the obtained X-ray data and peaks expected for the composition of the rope. For example, if the aiming is wrong, then the observed data will not have the diffraction peak known for a rope type, e.g., for a polyethylene rope. If the discrepancy, e.g., expressed as a number, exceed a threshold, this may trigger a warning and/or discarding of the data. A warning can trigger an engineer to verify if the sensor is aligned correctly, or if the system is configured for the correct rope type, etc.
For example, a problem encountered in experiments, which is addressed by this measure, is whether the diffraction data is obtained from a cover of the rope instead of the rope material itself. If a rope cover is of a different material, then this will be visible in an unexpected location of diffraction peaks. Even if the cover is of the same material as the rope than the peak intensities would be lower, e.g., below a threshold. In either situation, a warning may be given, or the system may refuse to use to take the data into account for a level of wear estimation. A rope is made using rope material. Rope material in the context of the classification herein is the high strength synthetic filaments. In other words, the rope material is the load bearing member of the rope, i.e., not a possible cover, a possible coating etc.
Figure 3c schematically shows an example of an embodiment of a training system 101 for a synthetic lengthy body monitoring system. Training system 101 may comprise a training unit 180 configured with a training algorithm that corresponds to the machine learnable model 160. For example, model 160 comprises a neural network, then training unit 180 may comprise a backpropagation algorithm. For example, if model 160 comprises an LDA model, then training unit 180 may comprise the corresponding LDA training algorithm.
Training system 101 has access to a training data set 181. For example, training data set 181 may comprise multiple pairs of a diffractive X-ray data and a corresponding level of wear of the synthetic lengthy body from which the sensor data was obtained. Typically, training unit 180 repeatedly iterates through the training set and repeatedly makes modification to the multiple parameters of machine learnable model 160 decreasing a distance between an actual prediction of model 160 and the desired prediction according to the training data set. Once the machine learnable model is sufficiently trained it may be installed in a computerto obtain a trained monitoring system.
Even after installing the trained model, it can continue to be trained. For example, in an arrangement in which a rope is continuously or frequently monitored, on the job a large amount of data will be obtained, as the level of wear of the synthetic lengthy body increases. It is possible to continue to make adjustments to the model even after it is first trained. For example, during use on one or multiple synthetic lengthy bodies additional sensor data is obtained, together with use information. The additional data may be used for further training the model.
One way to obtain training data is to repeatedly apply wear to a synthetic lengthy body. In an embodiment, measurements are taken during or in between the wear to obtain pairs of diffraction data and a level of wear. Another approach, which is advantageous in practice is to wear different parts of the rope by different amounts. When the wear has been applied the parts of the rope with different amounts of wear can be measured to obtain training data for different levels of wear. An advantage of this approach is that apply the wear can take place in a first setup until finish, after which in a second setup the measurements can be taken. Training data is thus obtained more efficiently. For example, an efficient way to apply different levels of wear to the same rope, is obtained when a first part of the lengthy body has a first level of wear and is not bent (no bent zone, NBZ), a second part of the lengthy body has a second level of wear and is single bent (single bent zone, SBZ) and a third part of the lengthy body had a third level of wear and is double bent (double bent zone, DBZ). This allows repeated bending over sheaf as illustrated in figure 10a and further discussed below. For example, the different levels of wear can be binned into one of multiple discrete classes, e.g., the classes no-bend, single-bend, double-bend, or the classes no bending, 50% bending and 100% bending, or the classes low wear, medium wear, high wear.
In the areas NBZ, SBZ, DBZ bending occurs a specified number of times per cycle; in this case 0, 1 , or 2 times per cycle. Typically, these are used in a cyclic test to failure, which could have thousands of cycles. For example, the double bend zone is bent twice per cycle, which is double as much as the SBZ area. In a first approximation, only the different relative amount of bending wear is taken into account. In a second analysis, a cyclic wear test may also impart wear due to tension in the wear, irrespective of the bending.
A wearing regime includes the different levels of wear used in the analyzing of the diffractive X-ray data and the classification attributed to it. Typically a wearing regime includes a number of classes of wear and the amount of wear corresponding to that class, i.e. a level of wear corresponding to that class. In the work described directly above, the wearing regime included three classes: NBZ, SBZ and DBZ. In embodiment(s) the wearing regime may be “no bending”, “50% bending” and “100% bending”. Where 100% bending corresponds to a broken rope. By modifying the wearing regime, as many classes of wear with as many percentages of bending can be created. In an embodiment, the wearing continues until the rope breaks. In an embodiment at least one class is the breaking level of the rope, i.e. the level of wear at which the rope breaks. An advantage of that approach is that the highest level of wear then corresponds to a fatal level of wear.
Figure 3d schematically shows an example of an embodiment of a monitoring device 102 for a synthetic lengthy body. Monitoring device 102 is similar to device 101 but takes clever advantage of the capability of machine learning models to accept multimodal data. In addition to monitoring device 101 , device 102 comprises a further sensor 113 configured to provide further sensor data 114. For example, the further data may be optical data and/or spectroscopy data. The model is configured to determine a combined value 162 indicating a level of wear from the further sensor data and the diffractive X-ray data. An advantage of this embodiment is that two otherwise independent indicators may be combined into a level of wear indication. The model may be trained to accept multiple sensor modalities, e.g., using a training set comprising tuples with multiple sensor data, and a desired level of wear data.
For example, as explained in the background an optical sensor may be used to obtain data regarding the surface of a synthetic lengthy body. For example, the data may be indicative of surface defects, surface abrasion, (damaged) filament pull-out, fiber misalignment and the like. The diffractive X-ray data is indicative of internal defects, e.g., internal abrasion and melt. Combining the two sources of data increases the robustness and/or reliability of the monitoring device.
Optionally, the further sensor may have a corresponding further source. For example, sensors like ultrasound or heat do not require a corresponding source, as the lengthy body emits the source itself. However, a source may be used for light, e.g., for visual inspection, e.g., a broad band light source, possibly using filters for spectroscopy, a narrow band light source, e.g., a laser, etc.
The further sensor data may be evaluated by a physical model, e.g., to provide a conservative prediction of wear, remaining lifetime, and the like. The further sensor data may also be subject to a trained machine learned model. The trained machine learned model may be the same model receiving the X-ray data, but may also be a separate model. In the latter case the outputs of the models may be combined.
Instead or in addition to using multiple sensor modalities, the monitoring device may be configured to apply the machine learnable model to multiple processed X-ray data obtained from multiple parts from the same lengthy body. In particular, obtained from parts of the rope that are close, or that have seen the same wear. There are at least two ways to do this. As a first example, the machine learnable model may be trained to accept two sets of diffractive X-ray data. An advantage of that approach is that the model can learn to extract any information that resides in correlations between the two samples. For example, one could take a first sample from the outside of a rope, while a second sample may be taken from inside the rope. For example, the two samples may be a predetermined distance from each other, e.g., 10 centimeters, 1 meter, etc., along the lengthy body.
For example, the samples may be close to each other on the same rope, e.g., within the same 0,5 meter length, preferably within the same 1 meter length, more preferably within the same 2 meter length along the longest axis of the synthetic lengthy body.
Another approach to using multiple samples is to apply the model to one sample at a time, but to combine the model results. For example, the model may provide level of wear data, possibly together with confidence data. The multiple wear values and confidence values may then be aggregated into an aggregated wear value and/or aggregated confidence value.
In an embodiment, the measurements obtained from sensor in the field may be sent, e.g., using a computer network, to a central database, e.g., in the cloud. This way a large collection of data can be obtained, which in turn may be used to train a model centrally. This can improve prediction for all in the field deployed testing machines.
Preferably, the obtained diffractive X-ray data is associated with ground-truth information, e.g., the actual wear level of the corresponding rope. Even without such ground-truth data such a data collection is useful, as it may be used to derive the typical progression of wear in a rope. For example, the data can be used to pre-train an autoencoder part of the model. Furthermore, useful information may be obtained from a level of change in the rope compared to a previous measurement, etc. For example, one may detect in this way, if a rope, or part thereof, fails earlier than usual, e.g., for similar types of ropes and applications, say on other ships. This information may also be used to give recommendations to a particular user, e.g., on rope care, e.g., avoiding wear to the rope. For example, by comparing the progression of wear in first rope to the progression of wear in one or more similar ropes used in similar applications, it can be established if the first rope has higher wear than expected. For example, one may give the recommendation that the sheaves need to be checked for damage, etc.
If a rope is used in combination with a crane, a lifetime may be attributed to the rope. Such attributed lifetime to the rope, with a given safe use margin, is typically in combination with the crane requirements amongst which a maximum load at which the crane in combination with that rope can be safely operated. This maximum load is as also referred to as crane rating.
An embodiment of the monitoring method for a synthetic lengthy body, may comprise as an additional step generating an indication for further use of the synthetic lengthy body. For example, if the method is used in combination with a load lifting device comprising a crane and a synthetic lengthy body arranged for mutual cooperation: provide a value to which the crane could temporarily be de-rated such that the synthetic lengthy body, such as a rope, is still safe for use with the de-rated crane. For example, a 150 metric ton crane may be de-rated to a 100 metric ton crane. A rope which would no longer be safe to be used on that crane for, say, a 150 metric ton lift job, may, e.g., still safely be used for a 100 metric ton lift job. After putting a new rope on the crane it can work again at full crane rating.
In an embodiment the present invention therefore provides a computer implemented monitoring method for a synthetic lengthy body having attributed lifetime with a given safe use margin, comprising obtaining diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in an X-ray sensor, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold, generating an indication for further use of the synthetic lengthy body, such as a remaining percentage of the attributed lifetime with a given safe use margin.
In another embodiment, the monitoring method is arranged for a load lifting device, the rope having an attributed lifetime with a given safe use margin, e.g., connected with the load rating of the crane, comprising obtaining diffractive X-ray data corresponding to the rope, wherein the diffractive X-ray data has been obtained from the rope by directing X-ray radiation to at least part of the rope and sensing the diffracted X-ray radiation in an X-ray sensor, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the rope, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold, generating an indication for further use of the load lifting device, such as providing a new attributed rope lifetime with given safe use margin at a lower load rating (de-rating) for the crane. In the various embodiments of devices 100, 101 , 102, communication interfaces may be selected from various alternatives. For example, the interface may comprise a network interface to a local or wide area network, e.g., the Internet, a storage interface to an internal or external data storage, a keyboard, an application interface (API), etc.
Device 100 may have a user interface, which may include well-known elements such as one or more buttons, a keyboard, display, touch screen, etc. The user interface may be arranged for accommodating user interaction for configuring the systems, training the model on a training set, or applying the system to new sensor data, etc.
Storage may be implemented as an electronic memory, say a flash memory, or magnetic memory, say hard disk or the like. Storage may comprise multiple discrete memories together making up the storage. The storage may be cloud storage.
Devices 100, 102 may be implemented in a single device or in a system. System 102 may be implemented in a single device. A single device in an example of a system. Typically, the system/de vices 100, 101 , 102 each comprise a microprocessor which executes appropriate software stored at the system; for example, that software may have been downloaded and/or stored in a corresponding memory, e.g., a volatile memory such as RAM or a non-volatile memory such as Flash. Alternatively, the systems may, in whole or in part, be implemented in programmable logic, e.g., as field- programmable gate array (FPGA). The systems may be implemented, in whole or in part, as a so-called application-specific integrated circuit (ASIC), e.g., an integrated circuit (IC) customized for their particular use. For example, the circuits may be implemented in CMOS, e.g., using a hardware description language such as Verilog, VHDL, etc. In particular, systems/device 100, 101 , 102 may comprise circuits for the evaluation of neural networks.
A processor circuit may be implemented in a distributed fashion, e.g., as multiple sub-processor circuits. A storage may be distributed over multiple distributed sub-storages. Part or all of the memory may be an electronic memory, magnetic memory, etc. For example, the storage may have volatile and a non-volatile part. Part of the storage may be read-only.
In an embodiment, a computer implemented monitoring method is provided, for a synthetic lengthy body comprising synthetic filaments, the method comprising obtaining diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X- ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X- ray radiation in an X-ray sensor providing information about crystalline morphology of the filaments, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold. Morphology herein is the shape, size, orientation, volume fraction of the crystals in the filaments, including their internal structure.
In an embodiment, a computer implemented monitoring method if provided for a synthetic lengthy body comprising synthetic filaments, the method comprising obtaining diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X- ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X- ray radiation in an X-ray sensor, the diffractive X-ray data indicating a diffraction image, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold. A diffraction image may be obtained from 1D information, including a diffraction pattern, such as obtained from an array I linear detector, or from 2D information. 2D information includes a diffraction pattern (nonreduced) or diffractogram (reduced, e.g., integrated to 1 D). Thus, a diffraction image includes a diffraction pattern and a diffractogram.
Figure 3e schematically shows an example of an embodiment of a monitoring method 300 for a synthetic lengthy body. Method 300 may be computer implemented and comprises obtaining (310) diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in an X-ray sensor, providing (315) a trained machine learnable model, processing (320) the diffractive X-ray data for input to the machine learnable model, applying (325) the machine learnable model to the processed diffractive X-ray data, the machine learnable model being configured to receive the processed diffractive X-ray data and to generate a value quantifying a level of wear in the part of the lengthy body, generating (330) a warning signal if the level of wear indicated by the value exceeds a wear threshold.
Figure 3f schematically shows an example of an embodiment of a training method 350 for a synthetic lengthy body monitoring system, Method 350 may be computer implemented and comprises obtaining (360) multiple diffractive X-ray data corresponding to multiple parts of one or more synthetic lengthy bodies, the multiple parts having been exposed to multiple levels of wear, wherein a diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffractive X-ray radiation in an X-ray sensor, obtaining (365) for the multiple X-ray data the level of wear to which the corresponding part of the lengthy body has been exposed, processing (370) the diffractive X-ray data for input to a machine learnable model, applying (375) a machine training algorithm corresponding to the machine learnable model to the multiple processed X-ray data and the corresponding multiple levels of wear to obtain a trained machine learnable model, the trained machine learnable model being configured to generate a value quantifying a level of wear in the part of the lengthy body, installing (380) the trained machine learnable model in a computer to obtain a trained monitoring system.
For example, the monitoring and training method may be computer implemented methods. For example, accessing training data, and/or receiving input data may be done using a communication interface, e.g., an electronic interface, a network interface, a memory interface, etc. For example, storing or retrieving parameters may be done from an electronic storage, e.g., a memory, a hard drive, etc., e.g., parameters of the networks. For example, applying a model to data of the training data set, and/or adjusting the stored parameters to train the network may be done using an electronic computing device, e.g., a computer. The model either during training and/or during applying may have multiple parameters, e.g., at least 50, 100, 1000 parameters or more.
Many different ways of executing the method are possible, as will be apparent to a person skilled in the art. For example, the order of the steps can be performed in the shown order, but the order of the steps can be varied or some steps may be executed in parallel. Moreover, in between steps other method steps may be inserted. The inserted steps may represent refinements of the method such as described herein, or may be unrelated to the method. For example, some steps may be executed, at least partially, in parallel. Moreover, a given step may not have finished completely before a next step is started.
Embodiments of the method may be executed using software, which comprises instructions for causing a processor system to perform method 300 and/or 350. Software may only include those steps taken by a particular sub-entity of the system. The software may be stored in a suitable storage medium, such as a hard disk, a floppy, a memory, an optical disc, etc. The software may be sent as a signal along a wire, or wireless, or using a data network, e.g., the Internet. The software may be made available for download and/or for remote usage on a server. Embodiments of the method may be executed using a bitstream arranged to configure programmable logic, e.g., a field-programmable gate array (FPGA), to perform the method.
It will be appreciated that the presently disclosed subject matter also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the presently disclosed subject matter into practice. The program may be in the form of source code, object code, a code intermediate source, and object code such as partially compiled form, or in any other form suitable for use in the implementation of an embodiment of the method. An embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the processing steps of at least one of the methods set forth. These instructions may be subdivided into subroutines and/or be stored in one or more files that may be linked statically or dynamically. Another embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the devices, units and/or parts of at least one of the systems and/or products set forth.
Figure 9a shows a computer readable medium 1000 having a writable part 1010 comprising a computer program 1020, the computer program 1020 comprising instructions for causing a processor system to perform a monitoring and/or training method, according to an embodiment. The computer program 1020 may be embodied on the computer readable medium 1000 as physical marks or by magnetization of the computer readable medium 1000. However, any other suitable embodiment is conceivable as well. Furthermore, it will be appreciated that, although the computer readable medium 1000 is shown here as an optical disc, the computer readable medium 1000 may be any suitable computer readable medium, such as a hard disk, solid state memory, flash memory, etc., and may be non-recordable or recordable. The computer program 1020 comprises instructions for causing a processor system to perform said monitoring and/or training method.
Figure 9b shows in a schematic representation of a processor system 1140 according to an embodiment of a monitoring and/or training device. The processor system comprises one or more integrated circuits 1110. The architecture of the one or more integrated circuits 1110 is schematically shown in Figure 9b. Circuit 1110 comprises a processing unit 1120, e.g., a CPU, for running computer program components to execute a method according to an embodiment and/or implement its modules or units. Circuit 1110 comprises a memory 1122 for storing programming code, data, etc. Part of memory 1122 may be read-only. Circuit 1110 may comprise a communication element 1126, e.g., an antenna, connectors or both, and the like. Circuit 1110 may comprise a dedicated integrated circuit 1124 for performing part or all of the processing defined in the method. Processor 1120, memory 1122, dedicated IC 1124 and communication element 1126 may be connected to each other via an interconnect 1130, say a bus. The processor system 1110 may be arranged for contact and/or contact-less communication, using an antenna and/or connectors, respectively.
For example, in an embodiment, processor system 1140, e.g., the monitoring and/or training device may comprise a processor circuit and a memory circuit, the processor being arranged to execute software stored in the memory circuit. For example, the processor circuit may be an Intel Core i7 processor, ARM Cortex-R8, etc. In an embodiment, the processor circuit may be ARM Cortex M0. The memory circuit may be an ROM circuit, or a non-volatile memory, e.g., a flash memory. The memory circuit may be a volatile memory, e.g., an SRAM memory. In the latter case, the device may comprise a non-volatile software interface, e.g., a hard drive, a network interface, etc., arranged for providing the software.
The memory 1122 may be considered to constitute a storage device. Memory 1122 may be an electronic memory. Various other arrangements will be apparent. Further, the memory 1122 may be considered to be “non-transitory machine-readable media.” As used herein, the term “non-transitory” will be understood to exclude transitory signals but to include all forms of storage, including both volatile and non-volatile memories.
While device 1140 is shown as including one of each described component, the various components may be duplicated in various embodiments. For example, the processor 1120 may include multiple microprocessors that are configured to independently execute the methods described herein or are configured to perform steps or subroutines of the methods described herein such that the multiple processors cooperate to achieve the functionality described herein. Further, where the device 1140 is implemented in a cloud computing system, the various hardware components may belong to separate physical systems. For example, the processor 1120 may include a first processor in a first server and a second processor in a second server.
Below several further optional refinements, details, and embodiments are illustrated.
Studying of the remaining lifetime of synthetic lengthy bodies with WAXS, in particular PE ropes, in particular UHMWPE ropes, depends on the transmission of X- rays through the rope material. To detect a WAXS signal a transmission higher than zero is needed. Furthermore, at constant primary beam intensity, the closer the transmission is to zero the longer time it will take to collect a meaningful scattering pattern. Figure 10c shows the fraction of X-ray transmitted through UHMWPE against X-ray energy and thickness of the material. The left y-axis show label 1210: X-Ray Energy (kV); the right y-axis show label 1230: Transmission; and the x-axis shows label 1220: Polyethylene Thickness (cm). The X-ray energies of common anode materials are shown on the right. The current experiment was done with a Cu anode at 8.04 keV with substrands of thickness 2 mm, where the transmission is approximately 10 %. To get similar transmission with the entire ropes having a thickness, i.e., equivalent diameter of 20 mm one may use an x-ray energy of 20 keV or more; for example, this could be realized with an Ag or a W anode. One may use a line-focus when increasing primary beam intensity.
As mentioned above, another solution to the low transmission problem when studying thick ropes is to use a tangential scan to avoid having to penetrate a lot of material. The tangential scan is further advantageous increases data quality by avoiding peak shadowing.
Various experiments were performed both at low and at high energy. A first set of experiments were performed at a low energy level. In these examples, experiments and embodiments below, the X-ray energy was 8 keV from a Cu source.
A continuous cyclic bend-over-sheave (CBOS) experiment was performed. A 12 stand circular rope made from Dyneema® UHMWPE filaments (see Figure 10b-sub a) was driven back and forth over a test sheave until failure. The test is designed so that the rope can be split into three sections which have experienced different wear histories. Figure 10a shows at A, a driving sheave at the top and a test sheave at the bottom. The driving sheave is driven alternatively in both directions to provide wear to the rope at the test sheave. Figure 10a shows at B a side view of the test sheave. In figure 10b a groove can be seen for guiding the rope. Figure 10a shows at panel C the test sheave, with a rope. Three regions are indicated marked: NBZ, SBZ and DBZ. These regions receive a different wearing regime.
No bend zone (NBZ): Only pre-stretching and axial tension- Estimated to have 100 % remaining lifetime after test.
Single bend zone (SBZ): Cycled in and out of test sheave- Estimated to have 50 % remaining lifetime after test
Double bend zone (DBZ): Always on the test sheave- Estimated to have 0 % remaining lifetime after test
The remaining lifetimes have been estimated to demonstrate the effectiveness of the embodiment, though in an application the remaining lifetimes could be different. For example, more accurate remaining lifetime estimates may be obtained by making continuous rope out of an NBZ, SBZ or DBZ section and test till failure.
After CBOS is complete rope sections are cut from each of the three zones (NBZ, SBZ and DBZ). A single strand was separated from each of the three zones. From each of the 3 strands 7 substrands were further extracted. Each of the 21 substrands were measured at 18 positions with wide-angle x-ray scattering (WAXS), for a total of 378 scans. The X-ray radiation went through 4 to 7 mm of rope material (UHMWPE), depending on the actual shape of the strand at the particular point.
Figure 10b shows at a) an entire rope section, at b) one of 12 strands from a rope, at c) all 7 sub-strands from a strand and at d) a single sub-strand. The ropes used here is a braided rope and so a single strand will vary between an internal and external cross-sectional position along the fiber axis. The strands are cut out so they both contain an external strand section, where the strand is positioned in the external part of the rope facing away from the test sheave, and an internal strand section where the strand is positioned internally in the rope. These zones are marked with EXT and INT in figure 10b.
WAXS data was measured on a Pilatus 300K detector (487 x 619 pixels). Data is azimuthally integrated to 200 points. Data is further pre-processed by normalizing each observation to sum to 1 , a log transformation and a standardization of each feature. X-ray energy was 8 keV. The azimuthally integration to 1 D was found to lose little information since the WAXS data is highly internally correlated and to have many regions without information. Figure 10d shows mean 1 D WAXS curves from all sub-strands for each of the three zones represented by mean curves (solid lines), standard deviation is indicated with shading.
In a first experiment, Linear discriminant analysis (LDA) machine learning was used for the machine learnable model. LDA finds the two linear discriminants, and the data is projected onto these discriminants. Observations are classified according to decision boundaries. A test classification accuracy: 68.3±0.6 % was obtained.
In a second experiment, Regularized Logistic Regression (RLR) machine learning was used. RLR finds characteristic features for each class. Data is projected onto these and observations are classified to the largest projection value. A test classification accuracy: 71.7 ± 0.6 % was obtained. Finally, RLR with fitted parameters was used for the machine learnable model. The results are summarized below
Figure imgf000059_0001
The data shows that rope condition can be predicted from low-energy WAXS data. In particular, when distinguishing high wear from low wear, the best model reaches nearly 90% accuracy.
A variety of other Machine Learning (ML) models can be used instead to predict the condition of ropes from WAXS data. Other classification models were successfully applied to solve the three-class classification problem, including: Random Forest (RF), Adaboost, and K nearest neighbors (KNN). However, of these approaches RLR and LDA outperformed the rest, in this case. The RLR has been used to test whether including more measurements of the samples tested will yield higher classification accuracy. 21 substrands in total were sampled, each at 18 positions. In total 378 measurements were used. 3-fold cross- validation has been performed. The data is partitioned into 3 subsets, each containing observations from 7 substrands: 2 are used for training and 1 is used for testing. This is done in 3 folds so that each combination of the subsets for training is used.
The models are trained on individual measurements. The models are evaluated using measurements from 1-9 positions on the same sub-strand (which of course always belong to the same class of wear treatment). The model tests on each position individually. Then the joint probability is calculated for each of the classes by multiplying the class probability estimates across the measurements. Finally, these are normalized, so that the class probability estimates sum to 1 . The class with the highest probability is selected as the predicted class/ classification outcome.
The normalized joint probability for a class C given N sets of probability estimates pk l where k is the specific class (NBZ, SBZ or DBZ) and / is the measurement number (running from 1 to N) is calculated with the formula:
Figure imgf000060_0001
Below is a hypothetical example with 3 measurements of the same sample with 3 different sets of estimated probability estimates. The final prediction is marked in bold.
Figure imgf000060_0002
The models are evaluated by the classification accuracy on the test set. This is the fraction of correct predictions on the test set. This is listed in the table below for each of the three cross-validation folds. Below the mean and variance of the classification accuracy is calculated. In the two lowest rows the mean and variance of the specificity and sensitivity are calculated. Each column uses a different number of observations to calculate the normalized joint probabilities before classifying the sample. The first column only uses 1 measurement, the next column uses two measurements to calculate the normalized joint probabilities and finally on the right side of the table nine measurements to calculate the normalized joint probabilities to do the classification.
Figure imgf000061_0001
Training was done over relatively small training set, but nevertheless, the data above shows that accuracy, specificity, and sensitivity all increase with the number of measurements. We have taken here accuracy as the fraction of correct predictions, specificity as the fraction of broken ropes that are identified as broken and sensitivity, as the fraction of healthy ropes that are identified as healthy.
When models are trained on low-energy data but lumping together the classes NBZ and SBZ in to a “healthy” class, keeping the DBZ separate as a “damaged” class a binary classification problem is obtained. The results in terms of classification accuracy, sensitivity and specificity can be seen in the table below:
Figure imgf000061_0002
Instead of a classification model a regressive model may be used. In this example, a remaining lifetime estimator is trained to produce a continuous estimate between 0 and 100 %. The NBZ data and DBZ data is translated into 100 % remaining lifetime and 0 % remaining lifetime, respectively. This is because the rope sees almost no wear in the NBZ and reaches complete failure in the DBZ. The SBZ data is estimated to have 50 % remaining lifetime. This regression model needs to make large interpolations, but this can be reduced by training with additional data.
In a first experiment the model is based on a regularized logistic regression classifier, wherein class probability estimates are extended to a continuous remaining lifetime prediction. In this example, the class probability estimates are multiplied by the remaining lifetime of that class and summed to give the remaining lifetime estimate. The generic formula for the remaining lifetime t given N classes each having a remaining lifetime tc and a class estimated probability of pc is: t =
Figure imgf000062_0001
• Pc
This is shown for a hypothetical example with the 3 bins currently available:
Figure imgf000062_0003
Furthermore, a range of other regression models have been used, including: linear regression, Lasso regression, Elastic net regression and random forest regression, etc. Accordingly, the RLR classifier can be extended to a continuous remaining lifetime predictor using the estimated class probabilities. Furthermore, different regularized logistic regression models have been shown to yield a similar R2. Experiments showed that regularization much improves linear regression for predicting remaining lifetime.
Another way to extend the interpolation of the RLR classifier is to calculate the variance of the estimated probabilities in terms of remaining lifetime and not just the mean. This may be done using the following equation: a2
Figure imgf000062_0002
- tc)2 • pc
With this quantity one can estimate the remaining lifetime and also quantify the confidence of that prediction. In an embodiment, only the two highest rates classes which are nearest neighbors are taken into account, even if multiple classes have nonvanishing probability. More complex regression schemes known in the art may also be applied. In an embodiment, an improved training of regression models uses an improved data collection scheme. For example, the time it took to break a rope, e.g., after running continuously on a sheave may be noted, after which other tests are run which are stopped at known fractions of that time (say at 10 %, 20 %, 30 %, 40 %, 50 %, 60 %, 70 %, 80 %, 90 %). Several WAXS measurements are made of each rope to use for testing purposes. In an alternatively embodiment, a constant temperature (water cooled) CBOS experiment is stopped at, e.g., 50% time to failure. This effectively reduces the DBZ to an SBZ, the NBZ remains an NBZ, but the SBZ now shows an intermediate level of wear which may be associated to 50%*50% = 25%, other intermediate times could be chosen to generate ropes of different wear level.
Data may be reduced as before using azimuthal integration and transforming the data as described for classification. A regression problem may be carried out using the reduced WAXS data as independent data (predictors) and remaining lifetime as the dependent variable.
With lower amounts of data linear classifiers appear to outperform nonlinear classifiers. In an embodiment, a non-linear model is used. An advantage of a non-linear model is that it may outperform linear models, especially when larger amounts of data are available. The evolution of morphology during mechanical wear, as most physical phenomena, is non-linear in nature. Non-linear models are therefore likely to improve regression performance (R2), when ropes of intermediate stages of wear are included in a regression analysis. It is highly likely that non-linear classifiers such as random forest regression, feed-forward neural networks or even convoluted neural networks (CNN), which are often used in image recognition tasks, would outperform linear models for larger amounts of training data.
When training models to predict remaining lifetime based on WAXS data a regularization method may be used to avoid overfitting. Especially, if there are many predictors compared to the number of observations. Common regularization methods are Lasso, Ridge or Elastic Net regularization.
A second set of experiments were performed at a high energy level. In the examples, experiments and embodiment below, the X-ray energy was 88 keV. 22 mm thick 12 strand circular ropes, i.e. 12 strand circular ropes having a diameter of 22 mm, of Dyneema SK78 yarns were manufactured forthis experiment (Dyneema® 1760 SK78, yarn tenacity 34.5 cN/dtex, filament tenacity 37 cN/dtex, Modulus 1190 cN/dtex, from DSM Protective Materials BV, The Netherlands). A healthy rope which has only been subject to pre-stretching, and a damaged rope which has been subject to internal and external abrasion and has been retired from commercial use. WAXS measurements were performed with a wavelength of 0.1405 A -1. A CdTe Pilatus 2M detector was used and placed at a distance of 2.243 m. The size of the X-ray beam was 100 x 100 \im. The complete ropes were placed on a motorized stage and were scanned in steps of 300 |imwith an exposure time of 10 seconds per scan. In total 70 scans were performed for each rope. Each observation was corrected for differences in transmission.
The data was masked and azimuthally integrated to 500 linearly spaced points in the Q-range from 12.5 to 22.5 nm -1 with the python pyFAl library, wherein Q is the wavevector transfer. The data was normalized to sum to 1 to account for differences in scattering volume effects. Figure 10e shows an example of an obtained WAXS image. The left and bottom axes are in pixels, the right axis shows intensity (a.u.)
The WAXS curves have also been processed in terms of fitting with the aim of deconvoluting each measurement to fewer and less correlated variables. This was done using the python Imfit library. For each of the 140 observations each Bragg feature was fitted by a Pseudo-Voigt function and the amorphous background was fitted by a combination of a Pseudo-Voigt and a linear function.
The fitted amplitudes were used to calculate the fraction of material in monoclinic and orthorhombic crystalline phase and the overall crystallinity. The fractions of material in crystalline phase was estimated by the integrated intensity of the WAXS curve cry from the crystalline peaks lcry divided by the total intensity I integrated:
(D
Figure imgf000064_0001
Wherein Qmax is the maximum experimentally accessible Q value. Qmin is the minimum experimentally accessible Q value. In an aspect, Qmax is 41.9 nnr1 and Qmin is 9.0 nnr1. The skilled person is aware that if Xc[%] is to be expressed in a range between 0 and 100%, then the right hand side of (1) needs to be multiplied by 100 and reads: maX IcrydQ <[%] = JO ill * 100 (1 *)
Vmin As input variables, overall crystallinity, monoclinic content, orthorhombic content and lastly the fraction between orthorhombic and monoclinic content were chosen. The peak positions Ghkl were used to estimate unit cell parameters. This was done using the following relation between peak position, Miller indices and unit cell parameters for the orthorhombic unit cell:
Figure imgf000065_0001
The relation for the monoclinic unit cell is:
Figure imgf000065_0002
Where f> = 107.9 for polyethylene. As input variables both aortho and bortho were estimated from the orthorhombic 200 and 110 peaks respectively. Furthermore, amono and cmono were estimated from the monoclinic 200 and 001 peaks respectively. The full width at half maximum (FWHM) of the peaks were used to estimate mean sizes of crystalline domains Thki perpendicular to a set of hkl planes. These mean sizes are estimated by the Scherrer equation:
Figure imgf000065_0003
K is the shape factor which in this work is set to 0.9 corresponding to a symmetric crystallite, phkt is the FWHM in 20 measured in radians for the hkl reflection and ehkt is the Bragg angle for the hkl reflection. As input variables T0110 , T0200 and rmooi were chosen since these are the most significant Bragg features.
In a first example, LDA was chosen as a machine learnable model. LDA determines linear combinations of features which separate classes of data for classification. LDA projects the data X onto a C-1 dimensional sub-space, where C is the number of classes, while maximizing the ratio of between-class variance and within-class variance. With two classes this sub-space will thus be spanned by a single linear discriminant w which is determined in the following way.
When data of two classes (class 0 and 1) with means p.o,
Figure imgf000065_0004
and covariance matrices Z 0, Z 0 are projected onto a vector w this will result in new class means w • p.o, w - and variances wr £ o w, w' ^ w. The ratio between-class variance and within- class variance may be defined by:
Figure imgf000066_0001
It can be shown that this ratio is maximized when w is proportional to:
Figure imgf000066_0002
LDA makes the assumption that the covariances for the two classes are equal (£ = £ 0 = £ .
Typically, these means and covariances are not known and have to be estimated with a training set using maximum likelihood estimators. When the number of training observations is small compared to the number of features the maximum likelihood estimator of the covariance matrix may be unstable. To stabilize the estimator one may introduce a shrinkage parameter y G [0,1] which shrinks the estimate of the covariance matrix towards diagonal matrix of variances.
Z (y) = (l - y) Z + ya2l (7)
A larger shrinkage parameter may be used when less training data is available compared to the number of predictors. After projection, the data is discriminated by a threshold c, typically set at the center between the two class means: c = w • (no + Hi) (8)
Stratified three-fold cross-validation has been performed, which ensures that both the training and test sets have equal fractions of observations from the healthy and damaged class. 2/3 of the data is stochastically selected for training (e.g., calculating w and c). The remaining 1/3 of the data is then used for independent testing, by applying the LDA classification process to these observations. The model is then evaluated by the test classification accuracy which is given by the percentage of correctly classified observations in the test set:
Accuracy [% J = 100
Figure imgf000066_0003
Where Ntest is the number of observations in the test set, yt is the true condition of observation i and y st is the estimated condition by the model (0 if damaged and 1 if healthy).
First LDA was performed using the 1 D WAXS data as input after normalization and centering by subtracting mean values for each Q-position. The shrinkage parameter was set to y = 0.5, which is relatively large due to the large number of predictors with respect to the number of training observations. The linear discriminant w is calculated using the training observations and Eq. 7. Figure 10f shows 1 D WAXS curves calculated from 70 scans of a healthy (bottom) and 70 scans of a damaged (top) rope. Figure 10g shows the linear discriminant coefficients from the first cross-validation fold. The areas corresponding to monoclinic and orthorhombic peaks are indicated with a shaded background. The higher the absolute value of a coefficient the higher the importance of the scattering signal at that Q-value in terms of distinguishing between healthy and damaged ropes. These coefficients are very similar across all three cross-validation folds.
LDA turns out to classify all observations in the training set correctly. The classifier was found to generalize well to independent data, since all observations in the test set are classified correctly. The test classification accuracies across all three cross- validation folds were 100 %.
Secondly LDA was performed using fitted parameters. The data was standardized by subtracting the mean and dividing by the standard deviation for each input parameter. The shrinkage parameter was set to y = 0.1, which is significantly smaller than with the 1 D WAXS input data due to the smaller number of predictors after the fitting procedure. The linear discriminant is calculated using the training observations and Eq. 7. The decision boundary is again calculated using Eq. 8. LDA is able to classify all observations in both the training set and the test set correctly. The test classification accuracies across all three cross-validation folds were 100 % which demonstrates that this predictive method based on fitted input parameters also generalizes to independent data.
Accordingly, the trained models distinguish between WAXS scans of the healthy and damaged rope using 1 D WAXS data and fitted parameters, respectively. To investigate the robustness of this classification method against noisier data the signal- to-noise ratio was lowered by simulating and adding Gaussian noise. It was demonstrated that the noise could be increased by a factor of three while still maintaining a test classification accuracy of 100 % using either 1 D WAXS data or fitted parameters as input. The mean test classification accuracies of both methods are above 75% even when the noise is increased by a factor of 10.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments.
In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. Use of the verb "comprise" and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. The article "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Expressions such as “at least one of’ when preceding a list or group of elements represent a selection of all or of any subset of elements from the list or group. For example, the expression, “at least one of A, B, and C” should be understood as including only A, only B, only C, both A and B, both A and C, both B and C, or all of A, B, and C. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
In the claims references in parentheses refer to reference signs in drawings of exemplifying embodiments or to formulas of embodiments, thus increasing the intelligibility of the claim. These references shall not be construed as limiting the claim.
The present invention includes the following embodiments Embodiment 1. A computer implemented monitoring method for a synthetic lengthy body, comprising obtaining diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in an X-ray sensor, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
Embodiment 2. A computer implemented monitoring method as in embodiment 1 , comprising providing a trained machine learnable model, processing the diffractive X-ray data for input to the machine learnable model, analyzing the diffractive X-ray data comprises applying the machine learnable model to the processed diffractive X-ray data, the machine learnable model being configured to receive the processed diffractive X-ray data and to generate the value quantifying a level of wear in the part of the lengthy body.
Embodiment 3. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the synthetic lengthy body is a synthetic rope or a synthetic chain.
Embodiment 4. A computer implemented monitoring method as in any one of the preceding embodiments, comprising obtaining an estimated remaining lifetime from the generated value.
Embodiment 5. A computer implemented monitoring method as in any one of the preceding embodiments, comprising applying the machine learnable model to multiple processed X-ray data obtained from multiple parts from the same lengthy body to obtain multiple values indicating multiple levels of wear, computing an aggregated level of wear estimation from the multiple values.
Embodiment 6. A computer implemented monitoring method as in any one of the preceding embodiments, comprising computing a confidence level for the level of wear or aggregated level of wear, generating a warning signal if the confidence level is below a confidence threshold and/or obtaining further X-ray data and recomputing the level of wear or aggregated level of wear.
Embodiment 7. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the lengthy body comprises multiple strands, the X-ray data being obtained by directing X-ray radiation to at least part of a strand isolated from the part of the lengthy body.
Embodiment 8. A computer implemented monitoring method as in any one of the preceding embodiments, wherein X-ray data is obtained by directing X-ray radiation tangentially to the lengthy body. Embodiment 9. A computer implemented monitoring method as in any one of the preceding embodiments, comprising applying recognition algorithm on the obtained sensor data, the obtained sensor data being discarded if the obtained sensor data does not correspond to valid diffractive X-ray data of a synthetic lengthy body.
Embodiment 10. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the processing of the X-ray data comprises applying a dimension reduction algorithm.
Embodiment 11. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the wear comprises filament damage accumulation.
Embodiment 12. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the monitoring method is a synthetic lengthy body health and/or synthetic lengthy body condition monitoring method.
Embodiment 13. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the X-ray radiation has a photon energy from at least 5 keV, for example, the X-ray radiation having a photon energy in one of the ranges: from 5 keV up to 400 keV, from 5 keV up to 35 keV, e.g., of 8 keV; or from 5 keV up to 100 keV; or from at least 50 keV, e.g., in the range from 50 keV up to 100 keV, e.g., of 88 keV, from 100 keV up to 230 keV; or from 100 keV up to 400 keV.
Embodiment 14. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the X-ray radiation has an energy sufficient to penetrate through the synthetic lengthy body.
Embodiment 15. A computer implemented monitoring method as in any one of the preceding embodiments, comprising obtaining further sensor data from a further sensor, wherein the further sensor data is one or more of optical data, acoustic data, ultrasonic data, emission data, and/or spectroscopy data, determining a combined value indicating a level of wear from the further sensor data and the diffractive X-ray data. Embodiment 16. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the synthetic lengthy body is a UHMWPE rope, in particular a braided UHMWPE rope comprising multiple strands.
Embodiment 17. A computer implemented monitoring method as in any one of the preceding embodiments, wherein the X-ray radiation is line-focused onto the at least part of the synthetic lengthy body.
Embodiment 18. A computer implemented training method (350) for a monitoring system configured for a synthetic lengthy body, the training method comprising obtaining (360) multiple diffractive X-ray data corresponding to multiple parts of one or more synthetic lengthy bodies, the multiple parts having been exposed to multiple levels of wear, wherein diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffractive X-ray radiation in an X-ray sensor, obtaining (365) for the multiple X-ray data the level of wear to which the corresponding part of the lengthy body has been exposed, processing (370) the diffractive X-ray data for input to a machine learnable model, applying (375) a machine training algorithm corresponding to the machine learnable model to the multiple processed X-ray data and the corresponding multiple levels of wear to obtain a trained machine learnable model, the trained machine learnable model being configured to generate a value quantifying a level of wear in the part of the lengthy body, installing (380) the trained machine learnable model in a computer to obtain a trained monitoring system.
Embodiment 19. Monitoring device for a synthetic lengthy body, the monitoring device comprising a sensor interface configured to obtain diffractive X-ray data from an X-ray sensor arranged for diffractive X-ray measurements corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in the X-ray sensor, a storage interface configured to provide a trained machine learnable model, processor subsystem configured for analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
Embodiment 20. Monitoring device for a synthetic lengthy body as in embodiment 19, wherein the processor subsystem is configured for processing the diffractive X-ray data for input to the machine learnable model, applying the machine learnable model to the processed diffractive X-ray data, the machine learnable model being configured to receive the processed diffractive X-ray data and to generate a value quantifying a level of wear in the part of the lengthy body,
Embodiment 21 . Monitoring device as in Embodiment 19 or 20 comprising the X- ray source and X-ray sensor, the lengthy body monitoring device comprising one or more spacers for spacing the lengthy body form the X-ray source and X-ray sensor while the lengthy body is submerged in water during operation of the X-ray source and X-ray sensor.
Embodiment 22. A load lifting device comprising a synthetic lengthy body, the load lifting device being arranged to lift a load with the synthetic lengthy body, and the monitoring device configured for the synthetic lengthy body as in any one of embodiments 19, 20 and 21 .
Embodiment 23. A training system for a synthetic lengthy body monitoring system, the training system comprising a training data interface configured for obtaining multiple diffractive X-ray data corresponding to multiple parts of one or more synthetic lengthy bodies, the multiple parts having been exposed to multiple levels of wear, wherein a diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffractive X-ray radiation in an X-ray sensor, and obtaining for the multiple X-ray data the level of wear to which the corresponding part of the lengthy body has been exposed, a processor interface configured for processing the diffractive X-ray data for input to a machine learnable model, applying a machine training algorithm corresponding to the machine learnable model to the multiple processed X-ray data and the corresponding multiple levels of wear to obtain a trained machine learnable model, the trained machine learnable model being configured to generate a value quantifying a level of wear in the part of the lengthy body, installing the trained machine learnable model in a computer to obtain a trained lengthy body monitoring system.
Embodiment 24. A transitory or non-transitory computer readable medium (1000) comprising data (1020) representing instructions, which when executed by a processor system, cause the processor system to perform the method according to any one of embodiments 1-18.

Claims

73 CLAIMS
Claim 1. A computer implemented monitoring method for a synthetic lengthy body, the synthetic lengthy body comprising synthetic filaments having a filament tenacity of at least 1 .5 N/tex, comprising obtaining diffractive X-ray data comprising Wide-angle X-ray scattering (WAXS) data, the diffractive X-ray data corresponding to the synthetic lengthy body, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by directing X- ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X- ray radiation in an X-ray sensor, analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
Claim 2. A computer implemented monitoring method as in Claim 1 , wherein the synthetic lengthy body comprises synthetic polyolefin filaments having a filament tenacity of at least 1.5 N/tex, preferably the synthetic lengthy body comprises synthetic polyethylene and/or polypropylene filaments having a filament tenacity of at least 1 .5 N/tex.
Claim 3. A computer implemented monitoring method as in any one of the preceding claims, wherein the synthetic lengthy body comprises synthetic UHMWPE filaments having a filament tenacity of at least 1 .5 N/tex.
Claim 4. A computer implemented monitoring method as in any one of the preceding claims, wherein the generating a value quantifying a level of wear includes analyzing a change in the crystal structure, preferably a change in the amount of monoclinic crystal phase, more preferably an increase of the amount of monoclinic crystal phase.
Claim 5. A computer implemented monitoring method as in any one of the preceding claims, comprising providing a trained machine learnable model, 74 processing the diffractive X-ray data for input to the machine learnable model, analyzing the diffractive X-ray data comprises applying the machine learnable model to the processed diffractive X-ray data, the machine learnable model being configured to receive the processed diffractive X-ray data and to generate the value quantifying a level of wear in the part of the lengthy body.
Claim 6. A computer implemented monitoring method as in Claim 5, wherein processing the diffractive X-ray data for input to the machine learnable model comprising applying a dimension reduction algorithm to obtain a 1 D diffraction image.
Claim 7. A computer implemented monitoring method as in any one of the preceding claims, wherein the diffractive X-ray data comprises a 2D or 1 D diffraction image obtained at a location of the synthetic lengthy body, and the method comprises applying a machine learnable model to the 2D or 1 D diffraction image, the machine learnable model being configured to generate a value quantifying a level of wear at the location of the lengthy body.
Claim 8. A computer implemented monitoring method as in any one of the preceding claims, wherein the synthetic lengthy body is a synthetic rope, a synthetic chain, a belt, a round sling, a splice, a strand, a cable, a cord, a ribbon, a strip, a hose, or a tube.
Claim 9. A computer implemented monitoring method as in any one of the preceding claims, comprising obtaining an estimated remaining lifetime from the generated value.
Claim 10. A computer implemented monitoring method as in any one of the preceding claims, comprising applying the machine learnable model to multiple processed X-ray data obtained from multiple parts from the same lengthy body to obtain multiple values indicating multiple levels of wear, computing an aggregated level of wear estimation from the multiple values.
Claim 11. A computer implemented monitoring method as in any one of the preceding claims, comprising computing a confidence level for the level of wear or aggregated level of wear, 75 generating a warning signal if the confidence level is below a confidence threshold and/or obtaining further X-ray data and recomputing the level of wear or aggregated level of wear.
Claim 12. A computer implemented monitoring method as in any one of the preceding claims, wherein the lengthy body comprises multiple strands, the X-ray data being obtained by directing X-ray radiation to at least part of a strand isolated from the part of the lengthy body.
Claim 13. A computer implemented monitoring method as in any one of the preceding claims, wherein X-ray data is obtained by directing X-ray radiation tangentially to the lengthy body.
Claim 14. A computer implemented monitoring method as in any one of the preceding claims, comprising applying a recognition algorithm on the obtained sensor data, the obtained sensor data being discarded if the obtained sensor data does not correspond to valid diffractive X-ray data of a synthetic lengthy body.
Claim 15. A computer implemented monitoring method as in any one of the preceding claims, wherein the wear comprises filament damage accumulation.
Claim 16. A computer implemented monitoring method as in any one of the preceding claims, wherein the monitoring method is a synthetic lengthy body health and/or synthetic lengthy body condition monitoring method.
Claim 17. A computer implemented monitoring method as in any one of the preceding claims, wherein the X-ray radiation has an energy sufficient to penetrate through the synthetic lengthy body.
Claim 18. A computer implemented monitoring method as in any one of the preceding claims, comprising obtaining further sensor data from a further sensor, wherein the further sensor data is one or more of optical data, acoustic data, ultrasonic data, emission data, and/or spectroscopy data, 76 determining a combined value indicating a level of wear from the further sensor data and the diffractive X-ray data.
Claim 19. A computer implemented monitoring method as in any one of the preceding claims, wherein the synthetic lengthy body is a UHMWPE rope, in particular a braided UHMWPE rope.
Claim 20. A computer implemented training method (350) for a monitoring system configured for a synthetic lengthy body, the synthetic lengthy body comprising multiple synthetic filaments having a filament tenacity of at least at least 1.5 N/tex, the training method comprising obtaining (360) multiple diffractive X-ray data comprising Wide-angle X-ray scattering (WAXS) data, the diffractive X-ray data corresponding to multiple parts of one or more synthetic lengthy bodies, the multiple parts having been exposed to multiple levels of wear, wherein diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffractive X-ray radiation in an X-ray sensor, obtaining (365) for the multiple X-ray data the level of wear to which the corresponding part of the lengthy body has been exposed, processing (370) the diffractive X-ray data for input to a machine learnable model, applying (375) a machine training algorithm corresponding to the machine learnable model to the multiple processed X-ray data and the corresponding multiple levels of wear to obtain a trained machine learnable model, the trained machine learnable model being configured to generate a value quantifying a level of wear in the part of the lengthy body, installing (380) the trained machine learnable model in a computer to obtain a trained monitoring system.
Claim 21. Monitoring device for a synthetic lengthy body, the synthetic lengthy body comprising multiple synthetic filaments having a filament tenacity of at least at least 1 .5 N/tex, the monitoring device comprising a sensor interface configured to obtain diffractive X-ray data from an X-ray sensor arranged for diffractive X-ray measurements corresponding to the synthetic lengthy body, the diffractive X-ray data comprising Wide-angle X-ray scattering (WAXS) data, wherein the diffractive X-ray data has been obtained from the synthetic lengthy body by 77 directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffracted X-ray radiation in the X-ray sensor, a storage interface configured to provide a trained machine learnable model, processor subsystem configured for analyzing the diffractive X-ray data, generating a value quantifying a level of wear in the part of the lengthy body, generating a warning signal if the level of wear indicated by the value exceeds a wear threshold.
Claim 22. Monitoring device for a synthetic lengthy body as in claim 21 , wherein the processor subsystem is configured for processing the diffractive X-ray data for input to the machine learnable model, applying the machine learnable model to the processed diffractive X-ray data, the machine learnable model being configured to receive the processed diffractive X-ray data and to generate a value quantifying a level of wear in the part of the lengthy body,
Claim 23. Monitoring device as in Claim 21 or 22 comprising the X-ray source and X-ray sensor, the lengthy body monitoring device comprising one or more spacers for spacing the lengthy body form the X-ray source and X-ray sensor while the lengthy body is submerged in water during operation of the X-ray source and X-ray sensor.
Claim 24. A load lifting device comprising a synthetic lengthy body, the load lifting device being arranged to lift a load with the synthetic lengthy body, and the monitoring device configured for the synthetic lengthy body as in any one of claims 21 , 22 and 23.
Claim 25. A training system for a synthetic lengthy body monitoring system, synthetic lengthy body comprising multiple synthetic filaments having a filament tenacity of at least at least 1 .5 N/tex, the training system comprising a training data interface configured for 78 obtaining multiple diffractive X-ray data corresponding to multiple parts of one or more synthetic lengthy bodies, the diffractive X-ray data comprising Wide-angle X-ray scattering (WAXS) data, the multiple parts having been exposed to multiple levels of wear, wherein a diffractive X-ray data has been obtained from the synthetic lengthy body by directing X-ray radiation to at least part of the synthetic lengthy body and sensing the diffractive X-ray radiation in an X-ray sensor, and obtaining for the multiple X-ray data the level of wear to which the corresponding part of the lengthy body has been exposed, a processor interface configured for processing the diffractive X-ray data for input to a machine learnable model, applying a machine training algorithm corresponding to the machine learnable model to the multiple processed X-ray data and the corresponding multiple levels of wear to obtain a trained machine learnable model, the trained machine learnable model being configured to generate a value quantifying a level of wear in the part of the lengthy body, installing the trained machine learnable model in a computer to obtain a trained lengthy body monitoring system.
Claim 26. A transitory or non-transitory computer readable medium (1000) comprising data (1020) representing instructions, which when executed by a processor system, cause the processor system to perform the method according to any one of claims 1-20.
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