WO2019075919A1 - 一种提升系统关键部件多状态健康监测装置及监测方法 - Google Patents

一种提升系统关键部件多状态健康监测装置及监测方法 Download PDF

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Publication number
WO2019075919A1
WO2019075919A1 PCT/CN2017/118989 CN2017118989W WO2019075919A1 WO 2019075919 A1 WO2019075919 A1 WO 2019075919A1 CN 2017118989 W CN2017118989 W CN 2017118989W WO 2019075919 A1 WO2019075919 A1 WO 2019075919A1
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WO
WIPO (PCT)
Prior art keywords
wire rope
friction
wear
bearing
health monitoring
Prior art date
Application number
PCT/CN2017/118989
Other languages
English (en)
French (fr)
Inventor
周公博
朱真才
李伟
彭玉兴
曹国华
周坪
舒鑫
郝本良
唐超权
罗林
Original Assignee
中国矿业大学
徐州煤矿安全设备制造有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中国矿业大学, 徐州煤矿安全设备制造有限公司 filed Critical 中国矿业大学
Priority to AU2017413084A priority Critical patent/AU2017413084B2/en
Priority to US16/304,127 priority patent/US10815098B2/en
Priority to RU2018140563A priority patent/RU2711767C1/ru
Publication of WO2019075919A1 publication Critical patent/WO2019075919A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/0006—Monitoring devices or performance analysers
    • B66B5/0018—Devices monitoring the operating condition of the elevator system
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B7/00—Other common features of elevators
    • B66B7/12—Checking, lubricating, or cleaning means for ropes, cables or guides
    • B66B7/1207—Checking means
    • B66B7/1215—Checking means specially adapted for ropes or cables
    • B66B7/1238—Checking means specially adapted for ropes or cables by optical techniques
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/0006—Monitoring devices or performance analysers
    • B66B5/0018—Devices monitoring the operating condition of the elevator system
    • B66B5/0031—Devices monitoring the operating condition of the elevator system for safety reasons
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • B66B5/0006—Monitoring devices or performance analysers
    • B66B5/0037—Performance analysers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B7/00—Other common features of elevators
    • B66B7/06—Arrangements of ropes or cables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B7/00—Other common features of elevators
    • B66B7/12—Checking, lubricating, or cleaning means for ropes, cables or guides
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B7/00—Other common features of elevators
    • B66B7/12—Checking, lubricating, or cleaning means for ropes, cables or guides
    • B66B7/1207—Checking means
    • B66B7/1215—Checking means specially adapted for ropes or cables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/952—Inspecting the exterior surface of cylindrical bodies or wires
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8883—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges involving the calculation of gauges, generating models

Definitions

  • the invention relates to a multi-state health monitoring device and a monitoring method for key components of a lifting system, and belongs to the field of mechanical health monitoring.
  • the mine hoisting system is a large-scale complex equipment integrating machine, electricity and liquid. It is responsible for the task of upgrading coal, vermiculite, decentralized materials, lifting personnel and equipment, and is called “throat of mine production”. Once a certain part fails, it is extremely detrimental to coal mine production and personnel safety. Wire ropes and bearings are key components of the lifting system and play a vital role in the safe operation of the lifting system. Therefore, it is necessary to adopt advanced techniques and means to monitor and evaluate the health status of key components of the mine lifting system in real time to prevent failures and serious accidents.
  • Wire ropes are widely used as lifting cables for mining machinery due to their light weight, low bending stiffness and high tensile strength.
  • Steel wire rope is a fragile and consumable item in mine lifting. Its safety and reliability is the basis for ensuring stable, efficient and safe operation of the mine lifting system.
  • the wire rope is frequently replaced to prevent malfunction, and the scrapped wire rope does not have a significant fault characteristic during operation. It can be seen that real-time monitoring of the running state of the wire rope, scientific replacement of the fault rope, extension of the service life of the wire rope and realization of health warning are of great significance.
  • Deep learning As a new branch of machine learning, deep learning is currently in rapid development, and has achieved fruitful research results in the fields of image and speech processing. Deep learning has powerful fault feature modeling and characterization capabilities, which can get rid of the dependence on a large number of signal processing techniques and diagnostic experience, and complete the adaptive extraction of fault features and intelligent diagnosis of health status. Deep learning has broad application prospects in the field of mechanical equipment health monitoring, and its application in the field of mine machinery equipment health monitoring is of great significance to the safe production of coal mines.
  • the invention provides a multi-state health monitoring device and a monitoring method for key components of a lifting system, simulates a fault condition of a steel wire rope and a bearing of a mine lifting system by using a health monitoring device, collects relevant information, and applies a deep learning method for intelligent fault diagnosis, thereby realizing mine lifting. Multi-state health monitoring of wire ropes and bearings in the system.
  • a multi-state health monitoring device for a key component of a lifting system comprising a frame, wherein the frame is welded by a plurality of rectangular steels to form a square structure, a wire rope is arranged around the periphery of the square structure, and the bottom layer of the square structure is installed from left to right.
  • the power system, the friction and wear device, the damage device and the tensioning device are installed with the bearing signal acquisition system, the tension sensor, the excitation device and the wire rope image acquisition system from the left to the right on the top layer of the square structure, and the wire rope passes through all the aforementioned devices in sequence.
  • the surrounding operation is carried out under the driving of the power system, and the monitoring of the running state of the wire rope is realized by all the foregoing devices or systems;
  • the power system includes a motor, a belt, a small pulley, a large pulley and a friction guide wheel assembly
  • the aforementioned friction guide wheel assembly includes a friction guide wheel, a horizontal shaft and a bearing housing
  • the motor Installed at the bottom end of the frame, the bearing seat is mounted on the frame adjacent to the motor, the horizontal axis is fixed on the bearing seat, the large pulley is fixed at the end of the horizontal shaft, and the friction guiding wheel is fixed and fixed at the middle of the horizontal axis.
  • the small pulley is mounted on the output shaft of the motor, and the small pulley and the large pulley are connected by the belt;
  • the friction wear device comprises a pressing cylinder, an action cylinder, an upper liner, a lower liner and a gasket mounting plate, and the action cylinder is horizontally mounted on the bottom layer of the rack, and comprises a rectangle perpendicular to the bottom layer of the rack inside the rack.
  • Steel, rectangular steel horizontally mounted compression cylinders wherein the upper gasket is fixedly mounted on the piston rod of the compression cylinder, and a movable friction wear slide is mounted on the frame, and the friction wear slide is located under the compression cylinder a frictional wear slide is mounted with a gasket mounting plate, and a lower gasket is mounted on the gasket mounting plate;
  • the damage device comprises a damage cylinder, a damage head and a support cushion.
  • the damage cylinder is horizontally mounted on a rectangular steel perpendicular to the bottom layer of the frame, and the damage head is movablely mounted on the piston rod of the damage cylinder.
  • the support pad is horizontally fixed on the bottom layer of the frame directly below the damage head;
  • the tensioning device comprises a tensioning cylinder, a tension and pressure sensor, a translating plate, a mounting bracket, a linear guide and a tensioning sliding seat, and the tensioning sliding seat is slidably mounted on the bottom layer of the frame, and simultaneously under the tensioning sliding seat
  • the utility model is provided with a linear guide fixed on the bottom layer of the rack, a fixing bracket is fixed on the tensioning sliding seat, and the translating plate is mounted on the mounting bracket, and the aforementioned tensioning cylinder is fixedly mounted on the bottom layer of the rack adjacent to the mounting bracket, and the tension and pressure sensor is One end is connected with the piston rod of the tensioning cylinder, and the other end is connected with the translating plate; a guiding wheel assembly is arranged on the surface of the translating plate, and the wire rope passes through the guiding wheel assembly to realize tensioning and relaxing action under the action of the tensioning cylinder;
  • the wire rope image acquisition system comprises a camera mount and a plurality of CCD industrial cameras.
  • the camera mount is a cylindrical structure, and the inner hexagon body is fixedly mounted therein, and a plurality of CCD industrial cameras are evenly distributed and installed in the inner hexagon body.
  • the aforementioned wire rope passes through the center of the cylindrical structure;
  • the bearing signal collecting system comprises a soundproof box, a noise sensor, an acceleration sensor and a sky wheel.
  • the soundproof box is installed and fixed on the top end of the rack, the sky wheel is installed inside the sound box, the noise sensor is installed inside the sound box, and the bottom is Fixed on the frame, the acceleration sensor is mounted on the outer bearing seat of the replaceable fault bearing, the wire rope passes through the isolation box and is wound on the balance wheel;
  • the horizontal axis is fixed to the bearing housing by a key and a screw;
  • the friction guiding wheel is fixed to the center of the horizontal axis by a key and a screw;
  • a first thermal imager for measuring a change in frictional wear temperature of the surface material and the wire rope in the state of the wire rope failure is mounted on one side of the friction guide wheel;
  • the upper gasket is fixedly mounted on the piston rod of the compression cylinder through a threaded hole; a linear guide fixed to the frame is mounted under the friction wear carriage, so that the friction wear slide is on the frame. mobile;
  • the aforementioned friction wear device is further provided with a second thermal imager for monitoring the relationship between the frictional wear condition and the temperature change during wear;
  • the aforementioned wire rope image acquisition system comprises three CCD industrial cameras with a center of a hexagonal center as a center, and three CCD industrial cameras are mounted at a uniform height of 120 degrees to be mounted in the inner hexagonal body;
  • the wire rope passes through the isolation box and is wound around the sky wheel, the aforementioned surface of the sky wheel contains a friction material; and a third thermal imager is installed in the sound insulation box of the bearing signal acquisition system;
  • a method for monitoring a wire rope by a multi-state health monitoring device for a key component of a lifting system first installing a wire rope containing a rust fault, and sequentially passing the wire rope containing the rust fault through a large pulley, a friction and wear device, a damage device, and tensioning
  • the device, the center position of the inner hexagon body, the excitation device, the tension sensor and the sky wheel form a complete circuit; firstly, the motor is started for the first time, so that the entire health monitoring device is slowly and evenly operated, so that the rusted portion of the wire rope runs to the friction and wear device.
  • the wire rope image acquisition system is turned on, and the image data information is extracted by multiple angles through three CCD industrial cameras; the wire rope image acquisition system transmits the collected images to the host computer, and the image is preprocessed.
  • a method for monitoring a bearing by a multi-state health monitoring device for a key component of the lifting system first installing a normal or faulty bearing on the outer bearing housing of the bearing signal acquisition system; passing the healthy steel wire rope through the large pulley, friction and wear
  • the device, the damage device, the tensioning device, the center position of the inner hexagon body, the excitation device, the tension sensor and the sky wheel form a complete circuit, and at the same time adjust the tension of the tensioning cylinder; start the motor to make the whole health monitoring device run at a constant speed
  • the third thermal imager cooperates with the acceleration sensor to extract noise and temperature data information; the collected noise and temperature data information is transmitted to the upper computer, and the noise signal is preprocessed and input into the previously trained RNN deep neural network model.
  • the upper computer calculates the data in parallel to realize the rapid identification and classification of the fault features in the bearing noise signal; the final result is output, and the fault type of the bearing is obtained.
  • the present invention has the following beneficial effects compared to the prior art:
  • the multi-state health monitoring device of the key component of the lifting system of the invention has the advantages of simple structure and convenient operation, can simulate the fault condition of the wire rope and the bearing in the lifting system, and realize the collection of relevant information, and does not need to collect data in depth;
  • the invention adopts a deep learning method, can get rid of dependence on a large number of signal processing technologies and diagnostic experience, completes adaptive extraction of fault features and intelligent diagnosis of health conditions, and has high diagnostic precision;
  • the present invention uses CNN (Convolutional Neural Network) deep neural network model to process wire rope image data, uses RNN (Recursive Neural Network) deep neural network model to process bearing noise data, and uses DBN (Deep Confidence Neural Network) deep neural network model to deal with bearing anomalies.
  • CNN Convolutional Neural Network
  • RNN Recursive Neural Network
  • DBN Deep Confidence Neural Network
  • Figure 1 is a schematic view of the overall structure of the present invention
  • Figure 2 is a front elevational view of the overall structure of the present invention.
  • FIG. 3 is a schematic structural view of a friction wear device and a damage device according to the present invention.
  • Figure 4 is a schematic structural view of a tensioning device of the present invention.
  • Figure 5 is a right side view of the overall structure of the present invention.
  • Figure 6 is a flow chart of the health monitoring method of the present invention.
  • FIG. 7 is a flowchart of a method for fault diagnosis based on deep learning according to the present invention.
  • 1 is the power system
  • 1-1 is the motor
  • 1-2 is the belt
  • 1-3 is the small pulley
  • 1-4 is the large pulley
  • 1-5 is the friction guide wheel
  • 1-6 is the horizontal axis 1-7 is the bearing housing
  • 2 is the friction and wear device
  • 2-1 is the compression cylinder
  • 2-2 is the action cylinder
  • 2-3 is the upper liner
  • 2-4 is the lower liner
  • 2-5 is the lining Pad mounting plate
  • 3 is the damage device
  • 3-1 is the damage cylinder
  • 3-2 is the damage head
  • 3-3 is the support pad
  • 4 is the tensioning device
  • 4-1 is the tensioning cylinder
  • 4- 2 is the tension and pressure sensor
  • 4-3 is the translating plate
  • 4-4 is the mounting bracket
  • 4-5 is the linear guide rail
  • 4-6 is the sliding seat
  • 5 is the wire rope image acquisition system
  • 5-1 is the camera mounting frame
  • 5 -2 is a CCD industrial camera
  • 6 is an excitation device
  • 7
  • the present invention includes the following technical features: power system 1, motor 1-1, belt 1-2, small pulley 1-3, large pulley 1-4, friction guide wheel 1-5 , horizontal axis 1-6, bearing housing 1-7, friction and wear device 2, compression cylinder 2-1, action cylinder 2-2, upper gasket 2-3, lower gasket 2-4, gasket mounting plate 2 -5, damage device 3, damage cylinder 3-1, damage head 3-2, support pad 3-3, tensioning device 4, tension cylinder 4-1, tension sensor 4-2, translation plate 4-3, mounting bracket 4-4, linear guide 4-5, slide 4-6, wire rope image acquisition system 5, camera mount 5-1, CCD industrial camera 5-2, excitation device 6, bearing signal acquisition system 7, soundproof box 7-1, noise sensor 7-2, acceleration sensor 7-3, sky wheel 7-4, tension sensor 8, wire rope 9, frame 10, first thermal imager 11-1, second thermal imaging Instrument 11-2, third thermal imager 11-3.
  • a multi-state health monitoring device for a key component of a lifting system comprises a frame, wherein the frame is welded by a plurality of rectangular steels to form a square structure, and a wire rope is arranged around the periphery of the square structure, and the bottom layer of the square structure is leftward.
  • the power system, the friction and wear device, the damage device and the tensioning device are installed in sequence on the right side, and the bearing signal acquisition system, the tension sensor, the excitation device and the wire rope image acquisition system are sequentially installed from the left to the right on the top layer of the square structure, and the wire rope passes through sequentially. All the foregoing devices or systems are operated by the power system to perform the surrounding operation, and the monitoring of the running state of the wire rope is realized by all the foregoing devices or systems;
  • the power system includes a motor, a belt, a small pulley, a large pulley and a friction guide wheel assembly, and the aforementioned friction guide wheel assembly includes a friction guide wheel and a horizontal direction.
  • the shaft and the bearing seat wherein the motor is installed at the bottom end of the frame, the bearing seat is mounted on the frame adjacent to the motor, the horizontal axis is fixed on the bearing seat, the large pulley is fixed at the end of the horizontal shaft, and the friction guiding The wheel is fixed in the middle of the horizontal axis, the small pulley is mounted on the output shaft of the motor, and the small pulley and the large pulley are connected by the belt, thereby transmitting the power of the motor to the friction guiding wheel assembly; it should be noted that The other guiding components in the power system are identical to the composition and installation of the friction guiding wheel assembly;
  • the friction and wear device comprises a pressing cylinder, an action cylinder, an upper liner, a lower liner and a gasket mounting plate, and the action cylinder is horizontally mounted on the bottom of the frame, at the machine
  • the inside of the frame includes rectangular steel perpendicular to the bottom of the frame, and the horizontally mounted compression cylinder is mounted on the rectangular steel.
  • the upper gasket is fixedly mounted on the piston rod of the compression cylinder, and the movable friction and wear sliding is also mounted on the frame.
  • a friction wear roller is located under the compression cylinder, and a friction mounting seat is mounted with a gasket mounting plate, the gasket mounting plate can move left and right along with the friction and wear sliding seat, and the lower gasket is mounted on the gasket mounting plate;
  • the friction and wear device simulates the friction and wear between the wire rope and the weighing pad under a certain pressing force: when the high-speed wear is simulated, the action cylinder is not moving and is in the original position, the power system drives the wire rope to run at high speed, and the pressing cylinder is loaded according to the program. And unloading to make the same length of wire rope wear; when simulating fretting wear, the power system does not move, the compression cylinder remains in the loaded state, and the action cylinder reciprocates to wear the same wire rope.
  • the second thermal imager is used for temperature measurement to obtain the relationship between the friction and wear conditions and the temperature change.
  • the damage device comprises a damage cylinder, a damage head and a support cushion
  • the damage cylinder is horizontally mounted on a rectangular steel perpendicular to the bottom layer of the frame to make a damage head. It can be installed on the piston rod of the damage cylinder. The angle of the damage head can be adjusted and the model can be replaced.
  • the support cushion is horizontally fixed on the bottom layer of the frame directly under the damage head to provide the damaged wire when the wire is punched. support;
  • the damage device is used to simulate the actual fault conditions on the surface of the wire rope, such as dents, broken wires, etc.: dents of different angles and different depths are produced under different impact forces or pressures, and combined with manual methods to manufacture different numbers and positions.
  • Broken wire for the image acquisition system of the wire rope to collect image information, and for the excitation device to detect the effective metal cross-sectional area;
  • the tensioning device comprises a tensioning cylinder, a tension and pressure sensor, a translating plate, a mounting bracket, a linear guide and a tensioning sliding seat, and the tensioning sliding seat is slidably mounted on the frame.
  • a linear guide fixed on the bottom layer of the frame is arranged at the same time, and the mounting bracket is fixed on the tensioning sliding seat, and the translational plate is mounted on the mounting frame, and the aforementioned tensioning cylinder is adjacent to the mounting bracket.
  • one end of the tension and pressure sensor is connected with the piston rod of the tensioning cylinder, and the other end is connected with the translating plate;
  • a sliding wheel assembly is arranged on the surface of the translating plate, and the wire rope passes through the guiding wheel assembly, in the sheet
  • the tensioning and relaxing action is realized under the action of the tight cylinder, and the tensioning force can be measured by the tension and pressure sensor;
  • the wire rope image acquisition system comprises a camera mount and a plurality of CCD industrial cameras.
  • the camera mount is a cylindrical structure, and the inner hexagon body is fixedly mounted therein.
  • the CCD industrial camera is evenly distributed and installed in the inner hexagonal body.
  • the aforementioned steel wire rope passes through the center of the cylindrical structure, and takes full picture of the surface information of the wire rope to prepare for image processing and fault diagnosis and identification;
  • the excitation device is mounted on the frame through the base, and the wire rope can be excited to detect the effective metal cross-sectional area of the wire rope, and the remaining strength (maximum breaking force) of the wire rope can be further obtained.
  • the bearing signal acquisition system includes a soundproof box, a noise sensor, an acceleration sensor and a sky wheel.
  • the soundproof box is installed and fixed at the top end of the rack, and the sky wheel is installed inside the sound box, and the noise sensor is installed.
  • the acceleration sensor is mounted on the outer bearing seat of the replaceable fault bearing, the wire rope passes through the isolation box and is wound on the sky wheel; the bearing signal acquisition system is performing noise on the bearing and During the vibration signal acquisition process, the noise and vibration signals of the normal and faulty bearings in the rotating operation state are simulated.
  • the third thermal imager is installed in the soundproof box of the bearing signal acquisition system to measure the bearing in the fault state. Temperature changes caused by wear.
  • the tension sensor is mounted on the frame, as shown in FIG. 2, at the position I, when the tension sensor is installed, the tension on the balance wheel in the bearing signal acquisition system can be measured.
  • the tension sensor is installed at the I and II positions shown in FIG. 2, the tension on the friction guide wheel can be measured;
  • the horizontal axis is fixed to the bearing housing by a key and a screw;
  • the friction guiding wheel is fixed to the center of the horizontal axis by a key and a screw;
  • a first thermal imager for measuring a change in frictional wear temperature of the surface material and the wire rope in the wire rope failure state is mounted on one side of the friction guide wheel;
  • the upper gasket is fixedly mounted on the piston rod of the compression cylinder through a threaded hole; a linear guide fixed to the frame is mounted under the friction wear carriage to make the friction wear slide on the frame mobile;
  • the aforementioned friction wear device is further provided with a second thermal imager for monitoring the relationship between the frictional wear condition and the temperature change during wear;
  • the aforementioned wire rope image acquisition system comprises three CCD industrial cameras with a center of a hexagonal center as a center, and three CCD industrial cameras are mounted at a uniform height of 120 degrees to be mounted in the inner hexagonal body;
  • the wire rope passes through the isolation box and is wound around the sky wheel, the aforementioned surface of the sky wheel contains a friction material; and a third thermal imager is installed in the sound insulation box of the bearing signal acquisition system;
  • the above-mentioned health monitoring device can perform multi-state health monitoring on key components of the system, such as initial setting, fault state simulation, fault information collection and processing, parallel computing based on deep learning method, and fault identification. And classification, etc.; the specific implementation is as follows:
  • Embodiment 1 A method for monitoring a wire rope by a multi-state health monitoring device for a key component of a lifting system, first installing a wire rope containing a rust fault, and sequentially passing the wire rope containing the rust fault through the large pulley, the friction and wear device, the damage device, The tensioning device, the center position of the inner hexagon body, the excitation device, the tension sensor and the sky wheel form a complete circuit; firstly, the motor is started for the first time, so that the entire health monitoring device is slowly and evenly operated, so that the rusted portion of the wire rope runs to the designated position.
  • the wire rope image acquisition system is turned on, and the image is extracted by three CCD industrial cameras.
  • Data information the wire rope image acquisition system transmits the acquired image to the host computer, and inputs the pre-trained CNN deep neural network model after the image is preprocessed, and the upper computer calculates the data in parallel to realize the rapid recognition of the fault in the image. Classification; the final output results in the type of failure of the wire rope;
  • Embodiment 2 The method of monitoring the bearing by the multi-state health monitoring device of the key component of the lifting system first installs a normal or faulty bearing on the outer bearing seat of the bearing signal collecting system (multiple faulty bearings can be selected to simulate different faults, Including inner ring, rolling element, cage and inner ring fault); passing healthy wire rope through large pulley, friction and wear device, damage device, tension device, central position of internal hexagon, excitation device, tension sensor And the sky wheel, forming a complete circuit, and adjusting the tension of the tensioning cylinder (multiple tensioning force can be selected to simulate the tension of the wire rope under different loads); start the motor to make the whole health monitoring device run at a constant speed (multiple speeds can be Selecting and simulating different running speeds, the third thermal imager and the acceleration sensor work together to extract noise and temperature data information; the collected noise and temperature data information is transmitted to the host computer, and the noise signal is pre-processed and input is trained in advance.
  • the host computer performs parallel
  • Embodiment 3 The method of monitoring the bearing by the multi-state health monitoring device of the key component of the lifting system first installs a normal or faulty bearing on the outer bearing seat of the bearing signal collecting system (multiple faulty bearings can be selected to simulate different faults, Including inner ring, rolling element, cage and inner ring fault); passing healthy wire rope through large pulley, friction and wear device, damage device, tension device, central position of internal hexagon, excitation device, tension sensor And the sky wheel, forming a complete circuit, and adjusting the tension of the tensioning cylinder (multiple tensioning force can be selected to simulate the tension of the wire rope under different loads); start the motor to make the whole health monitoring device run at a constant speed (multiple speeds can be Selecting and simulating different running speeds, the third thermal imager and the acceleration sensor cooperate to extract the bearing seat vibration signal and temperature data information; the collected bearing seat vibration signal is transmitted to the upper computer, and the vibration signal is preprocessed and input beforehand.
  • the host computer performs parallel calculation
  • the fault diagnosis method based on deep learning in the upper computer takes the wire rope surface image fault diagnosis as an example: firstly, deep learning of the wire rope surface image fault information under known working conditions is performed to obtain the training sample reconstruction signal. Then, the deep learning of the image information of the surface of the wire rope under the condition to be tested is performed to obtain the reconstructed signal of the test sample; finally, the classifier (such as support vector machine, shallow neural network, etc.) is used to reconstruct the signal of the test sample and The training sample reconstruction signal is matched, and the fault identification and classification are performed according to the reconstructed signal similarity, thereby obtaining the fault diagnosis result.
  • GPU parallel acceleration calculation is used in the deep learning process to improve computational efficiency.
  • a multi-state health monitoring method for key components of the lifting system after the fault type determination of the wire rope surface is completed by the CNN deep neural network model, the residual strength of the fault measured by the excitation device is combined.
  • the real-time tension at the fault measured by the tension sensor (the difference between the tension sensors on both sides of the fault) can be used to obtain the dynamic safety factor of the rope.
  • the minimum safety factor of the rope is used as the safety evaluation standard and compared with the specified safety factor. Therefore, the safety of the wire rope is evaluated: when the minimum safety factor of the whole rope is not less than the specified safety factor, the wire rope is in a healthy state.
  • F Ri is the residual strength of the wire rope fault at the i-th.
  • the remaining strength is the excitation of the wire rope by the excitation device to detect the effective metal cross-sectional area of the wire rope, and further obtain the maximum breaking force of the wire rope, and the breaking force is
  • the residual strength, F i is the measured tension at the fault of the wire rope at the i-th, and the minimum safety factor of the whole rope is taken as the safety evaluation standard:
  • min ⁇ F Ri ⁇ is the minimum residual strength of the full wire rope
  • max ⁇ F i ⁇ is the maximum measured tension of the whole rope
  • [k a ] is the safety factor specified by the regulation
  • the value is the sum of the designed breaking force and the maximum static load. According to this formula, the minimum residual strength and the maximum measured tension in i are found. If the ratio is greater than or equal to the safety factor specified in the regulations, the wire rope is healthy.
  • a multi-state health monitoring method for key components of the lifting system is performed by RNN and DBN deep neural network models to determine the type of bearing fault, and the diagnostic process and results can be compared and analyzed. Provide a basis for improving the health monitoring method of the rotating machinery and the selection of the deep neural network model.
  • a multi-state health monitoring device and method for key components of the lifting system can also be used to study the relationship between the following parameters: wire rope failure condition and failure degree and friction temperature, tension, residual strength, Correlation between parameters such as contact force of the sky wheel; relationship between bearing fault condition and fault degree and parameters such as wear temperature, noise, radial load, and rotational speed.
  • the relationship between the above parameters plays a guiding role in the evaluation of the health status of the steel wire rope and the bearing, and the degree of the fault can be judged on the basis of the type of the fault.
  • connection may be a direct connection between components or an indirect connection between components through other components.

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Abstract

一种提升系统关键部件多状态健康监测装置及监测方法，包括机架，所述的机架由多个矩形钢焊接形成方形结构，围绕方形结构外围布设有钢丝绳，在方形结构的底层由左向右依次安装动力系统、摩擦磨损装置、制损装置和张紧装置，在方形结构的顶层由左向右依次安装轴承信号采集系统、张力传感器、励磁装置和钢丝绳图像采集系统，钢丝绳顺次穿过前述所有装置或系统，在动力系统的带动下进行环绕运行，通过前述所有装置或系统实现钢丝绳运行状态的监测；利用健康监测装置模拟矿井提升系统钢丝绳及轴承的故障状况并采集相关信息，应用深度学习方法进行智能故障诊断，从而实现矿井提升系统中钢丝绳及轴承的多状态健康监测。

Description

一种提升系统关键部件多状态健康监测装置及监测方法 技术领域
本发明涉及一种提升系统关键部件多状态健康监测装置及监测方法，属于机械健康监测领域。
背景技术
矿井提升系统是集机、电、液于一体的大型复杂设备，担负提升煤炭、矸石，下放材料，升降人员与设备的任务，被称为“矿山生产的咽喉”。一旦某一部位发生故障，对煤矿生产和人员安全都极为不利。钢丝绳和轴承作为提升系统的关键部件，在提升系统的安全运行中起着至关重要的作用。所以，有必要采取先进的技术和手段对矿井提升系统关键部件的健康状况进行实时监测和评估，以预防故障和恶性事故的发生。
钢丝绳因重量轻、抗弯刚度小、抗拉强度高等优点，被广泛用作矿山机械的提升缆绳。钢丝绳作为矿井提升中的易坏和易耗品，其安全可靠性是保障矿井提升系统稳定、高效和安全运行的基础。然而钢丝绳在实际使用中为防止故障而更换频繁，且报废的钢丝绳在运行过程中并未发生显著的故障特征。由此可见，对钢丝绳运行状态进行实时监测，科学更换故障绳、延长钢丝绳使用寿命和实现健康预警具有重要的意义。
轴承作为旋转机械的重要部件，在矿井提升系统中应用广泛，例如动力电机、减速器、天轮装置、主轴装置等，其缺陷会导致设备异常振动和噪声，甚至造成设备损坏，所以其健康状况也直接影响着提升系统的安全可靠性。然而，现有故障诊断技术如快速傅里叶变换(FFT)、小波变换和经典模态分解(EMD)等故障特征建模及表征能力较差，严重依赖人为提取故障特征，在机械故障诊断中存在着较大不足。所以，采用先进智能诊断技术对旋转机械中轴承的运行状态进行实时监测，对提升系统的安全运行具有重要的意义。
深度学习作为机器学习的一个新的分支，目前处于快速发展当中，在图像和语音处理等领域取得了丰硕的研究成果。深度学习具有强大的故障特征建模及表征能力，其能够摆脱对大量信号处理技术及诊断经验的依赖，完成故障特征的自适应提取与健康状况的智能诊断。深度学习在机械装备健康监测领域具有广阔的应用前景，而应用在矿山机械装备健康监测领域，对煤矿的安全生产意义重大。
发明内容
本发明提供一种提升系统关键部件多状态健康监测装置及监测方法，利用健康监测装置模拟矿井提升系统钢丝绳及轴承的故障状况并采集相关信息，应用深度学习方法进行智能故 障诊断，从而实现矿井提升系统中钢丝绳及轴承的多状态健康监测。
本发明解决其技术问题所采用的技术方案是：
一种提升系统关键部件多状态健康监测装置，包括机架，所述的机架由多个矩形钢焊接形成方形结构，围绕方形结构外围布设有钢丝绳，在方形结构的底层由左向右依次安装动力系统、摩擦磨损装置、制损装置和张紧装置，在方形结构的顶层由左向右依次安装轴承信号采集系统、张力传感器、励磁装置和钢丝绳图像采集系统，钢丝绳顺次穿过前述所有装置或系统，在动力系统的带动下进行环绕运行，通过前述所有装置或系统实现钢丝绳运行状态的监测；
作为本发明的进一步优选，所述的动力系统包括电机、皮带、小带轮、大带轮和摩擦导向轮组件，前述的摩擦导向轮组件包括摩擦导向轮、横轴和轴承座，其中，电机安装在机架底层端部处，轴承座与电机相邻安装在机架上，横轴固定在轴承座上，大带轮安装固定在横轴端头，摩擦导向轮安装固定在横轴中间部位，小带轮安装在电机的输出轴上，小带轮和大带轮通过皮带进行连接；
所述的摩擦磨损装置包括压紧缸、动作缸、上衬垫、下衬垫和衬垫安装板，动作缸水平安装在机架底层上，在机架内部包括与机架底层相垂直的矩形钢，矩形钢上水平安装压紧缸，其中，上衬垫固定安装在压紧缸的活塞杆上，在机架上还安装可移动的摩擦磨损滑座，摩擦磨损滑座位于压紧缸下方，摩擦磨损滑座上安装有衬垫安装板，下衬垫安装在衬垫安装板上；
所述的制损装置包括制损缸、制损头和支撑衬垫，制损缸水平安装在与机架底层相垂直的矩形钢上，制损头可活动安装在制损缸的活塞杆上，支撑衬垫水平固定在位于制损头正下方的机架底层上；
所述的张紧装置包括张紧缸、拉压传感器、平移板、安装架、直线导轨和张紧滑座，张紧滑座可滑动安装在机架底层上，在张紧滑座的下方同时设有固定在机架底层上的直线导轨，张紧滑座上固定安装架，平移板安装在安装架上，前述的张紧缸与安装架相邻固定安装在机架底层上，拉压传感器的一端与张紧缸的活塞杆连接，其另一端与平移板相连接；在平移板表面设有导向轮组件，钢丝绳穿过导向轮组件，在张紧缸作用下实现张紧与放松动作；
所述的钢丝绳图像采集系统包括相机安装架和多个CCD工业相机，所述的相机安装架为圆柱体结构，其内固定安装内六角体，多个CCD工业相机均匀分布安装在内六角体内，前述的钢丝绳由圆柱体结构中心穿过；
所述的轴承信号采集系统包括隔音箱、噪声传感器、加速度传感器和天轮，隔音箱安装固定在机架顶层端头，天轮安装在隔音箱内部，噪声传感器安装在隔音箱内部，且其底部固 定在机架上，加速度传感器安装在可更换故障轴承的外侧轴承座上，钢丝绳穿过隔离箱并缠绕在天轮上；
作为本发明的进一步优选，横轴通过键和螺钉固定在轴承座上；摩擦导向轮通过键和螺钉安装固定在横轴中央；
作为本发明的进一步优选，在摩擦导向轮的一侧安装有用于测量摩擦导向轮在钢丝绳故障状态下表面材料与钢丝绳摩擦磨损温度变化的第一热成像仪；
作为本发明的进一步优选，上衬垫通过螺纹孔固定安装在压紧缸的活塞杆上；在摩擦磨损滑座下方安装有固定在机架上的直线导轨，使摩擦磨损滑座在机架上移动；
作为本发明的进一步优选，前述的摩擦磨损装置还设有用于监测在磨损过程中摩擦磨损状况与温度变化间关系的第二热成像仪；
作为本发明的进一步优选，前述的钢丝绳图像采集系统包括三个CCD工业相机，以内六角中心为圆心，三个CCD工业相机两两呈120度安装均匀分布安装在内六角体内；
作为本发明的进一步优选，钢丝绳穿过隔离箱并缠绕在天轮上，前述的天轮表面含有摩擦材料；在轴承信号采集系统的隔音箱内安装第三热成像仪；
一种通过提升系统关键部件多状态健康监测装置对钢丝绳进行监测的方法，首先安装含锈蚀故障的钢丝绳，将含锈蚀故障的钢丝绳依次穿过大带轮、摩擦磨损装置、制损装置、张紧装置、内六角体中心位置处、励磁装置、张力传感器以及天轮，形成完整回路；先第一次启动电机，使整个健康监测装置进行缓慢匀速运行，使钢丝绳的锈蚀部分运行到与摩擦磨损装置和制损装置的不接触处；关闭电机，启动摩擦磨损装置和制损装置，在钢丝绳上分别制造磨损、凹痕以及断丝故障，并且使各种故障等间距分开；二次启动电机，使整个监测装置再次进行缓慢匀速运行，此时开启钢丝绳图像采集系统，通过三个CCD工业相机进行多角度提取图片数据信息；钢丝绳图像采集系统将采集到的图片传入上位机，对图像预处理后输入事先训练好的CNN深度神经网络模型中，上位机对数据进行并行计算，实现图像中故障的快速识别和分类；最后输出结果，得到钢丝绳的故障类型；
一种通过提升系统关键部件多状态健康监测装置对轴承进行监测的方法，首先在轴承信号采集系统的外侧轴承座上安装正常或者故障的轴承；将健康的钢丝绳依次穿过大带轮、摩擦磨损装置、制损装置、张紧装置、内六角体中心位置处、励磁装置、张力传感器以及天轮，形成完整回路，同时调整张紧缸的张紧力；启动电机，使整个健康监测装置匀速运行，通过第三热成像仪与加速度传感器共同配合提取噪声和温度数据信息；将采集到的噪声和温度数据信息传入上位机，对噪声信号预处理后输入事先训练完毕的RNN深度神经网络模型中，上 位机对数据进行并行计算，实现轴承噪声信号中故障特征的快速识别和分类；最后结果输出，得到轴承的故障类型。
通过以上技术方案，相对于现有技术，本发明具有以下有益效果：
本发明的提升系统关键部件多状态健康监测装置，结构简单，操作方便，可模拟出提升系统中钢丝绳及轴承故障状况，并实现相关信息的采集，无需深入现场采集数据；
本发明采用深度学习方法，能够摆脱对大量信号处理技术及诊断经验的依赖，完成故障特征的自适应提取与健康状况的智能诊断，具有较高的诊断精度；
本发明使用CNN(卷积神经网络)深度神经网络模型处理钢丝绳图像数据，使用RNN(递归神经网络)深度神经网络模型处理轴承噪声数据，使用DBN(深度置信神经网络)深度神经网络模型处理轴承异常振动数据，研究方法涵盖了几种经典深度学习算法，方法和结论可推广到其他矿山机械装备健康监测与故障诊断当中。
附图说明
下面结合附图和实施例对本发明进一步说明。
图1为本发明的整体结构示意图；
图2为本发明的整体结构的主视图；
图3为本发明的摩擦磨损装置及制损装置结构示意图；
图4为本发明的张紧装置结构示意图；
图5为本发明的整体结构的右视图；
图6为本发明的健康监测方法流程图；
图7为本发明基于深度学习的故障诊断方法流程图。
图中：1为动力系统，1-1为电机，1-2为皮带，1-3为小带轮，1-4为大带轮，1-5为摩擦导向轮，1-6为横轴，1-7为轴承座，2为摩擦磨损装置，2-1为压紧缸，2-2为动作缸，2-3为上衬垫，2-4为下衬垫，2-5为衬垫安装板，3为制损装置，3-1为制损缸，3-2为制损头，3-3为支撑衬垫，4为张紧装置，4-1为张紧缸，4-2为拉压传感器，4-3为平移板，4-4为安装架，4-5为直线导轨，4-6为滑座，5为钢丝绳图像采集系统，5-1为相机安装架，5-2为CCD工业相机，6为励磁装置，7为轴承信号采集系统，7-1为隔音箱，7-2为噪声传感器，7-3为加速度传感器，7-4为天轮，8为张力传感器，9为钢丝绳，10为机架，11-1为第一热成像仪，11-2为第二热成像仪，11-3为第三热成像仪。
具体实施方式
现在结合附图对本发明作进一步详细的说明。这些附图均为简化的示意图，仅以示意方式说明本发明的基本结构，因此其仅显示与本发明有关的构成。
如图1-图7所示，本发明包括以下技术特征：动力系统1，电机1-1，皮带1-2，小带轮 1-3，大带轮1-4，摩擦导向轮1-5，横轴1-6，轴承座1-7，摩擦磨损装置2，压紧缸2-1，动作缸2-2，上衬垫2-3，下衬垫2-4，衬垫安装板2-5，制损装置3，制损缸3-1，制损头3-2，支撑衬垫3-3，张紧装置4，张紧缸4-1，拉压传感器4-2，平移板4-3，安装架4-4，直线导轨4-5，滑座4-6，钢丝绳图像采集系统5，相机安装架5-1，CCD工业相机5-2，励磁装置6，轴承信号采集系统7，隔音箱7-1，噪声传感器7-2，加速度传感器7-3，天轮7-4，张力传感器8，钢丝绳9，机架10，第一热成像仪11-1，第二热成像仪11-2，第三热成像仪11-3。
本发明的一种提升系统关键部件多状态健康监测装置，包括机架，所述的机架由多个矩形钢焊接形成方形结构，围绕方形结构外围布设有钢丝绳，在方形结构的底层由左向右依次安装动力系统、摩擦磨损装置、制损装置和张紧装置，在方形结构的顶层由左向右依次安装轴承信号采集系统、张力传感器、励磁装置和钢丝绳图像采集系统，钢丝绳顺次穿过前述所有装置或系统，在动力系统的带动下进行环绕运行，通过前述所有装置或系统实现钢丝绳运行状态的监测；
图1、图2所示，作为本发明的进一步优选，所述的动力系统包括电机、皮带、小带轮、大带轮和摩擦导向轮组件，前述的摩擦导向轮组件包括摩擦导向轮、横轴和轴承座，其中，电机安装在机架底层端部处，轴承座与电机相邻安装在机架上，横轴固定在轴承座上，大带轮安装固定在横轴端头，摩擦导向轮安装固定在横轴中间部位，小带轮安装在电机的输出轴上，小带轮和大带轮通过皮带进行连接，从而将电机的动力传递到摩擦导向轮组件上；需要注意的是，动力系统中其他导向组件与摩擦导向轮组件的组成和安装方式均一致；
图1、图2和图3所示，所述的摩擦磨损装置包括压紧缸、动作缸、上衬垫、下衬垫和衬垫安装板，动作缸水平安装在机架底层上，在机架内部包括与机架底层相垂直的矩形钢，矩形钢上水平安装压紧缸，其中，上衬垫固定安装在压紧缸的活塞杆上，在机架上还安装可移动的摩擦磨损滑座，摩擦磨损滑座位于压紧缸下方，摩擦磨损滑座上安装有衬垫安装板，衬垫安装板可随着摩擦磨损滑座一起左右移动，下衬垫安装在衬垫安装板上；
摩擦磨损装置在一定的压紧力下，模拟钢丝绳与称垫间的摩擦磨损状况：当模拟高速磨损时，动作缸不动且处于原位，动力系统带动钢丝绳高速运行，压紧缸根据程序加载和卸载以使同一段钢丝绳磨损；当模拟微动磨损时，动力系统不动，压紧缸保持加载状态，动作缸往复运动，以使同一段钢丝绳磨损。磨损过程中，同时使用第二热成像仪进行测温，以得到摩擦磨损状况与温度变化间的关系。
图1、图2和图3所示，所述的制损装置包括制损缸、制损头和支撑衬垫，制损缸水平 安装在与机架底层相垂直的矩形钢上，制损头可活动安装在制损缸的活塞杆上，制损头角度可调、型号可更换，支撑衬垫水平固定在位于制损头正下方的机架底层上，以在制损头冲压钢丝绳时提供支撑；
制损装置用于模拟钢丝绳表面的实际故障状况，如凹痕、断丝等：在不同冲击力或压力下，制造不同角度和不同深度的凹痕，并结合人工方法，制造不同根数和位置的断丝，以供钢丝绳图像采集系统采集图像信息，并供励磁装置检测有效金属截面积；
图1、图2和图4所示，所述的张紧装置包括张紧缸、拉压传感器、平移板、安装架、直线导轨和张紧滑座，张紧滑座可滑动安装在机架底层上，在张紧滑座的下方同时设有固定在机架底层上的直线导轨，张紧滑座上固定安装架，平移板安装在安装架上，前述的张紧缸与安装架相邻固定安装在机架底层上，拉压传感器的一端与张紧缸的活塞杆连接，其另一端与平移板相连接；在平移板表面设有导向轮组件，钢丝绳穿过导向轮组件，在张紧缸作用下实现张紧与放松动作，并可通过拉压传感器测出张紧力；
图1、图2和图5所示，所述的钢丝绳图像采集系统包括相机安装架和多个CCD工业相机，所述的相机安装架为圆柱体结构，其内固定安装内六角体，多个CCD工业相机均匀分布安装在内六角体内，前述的钢丝绳由圆柱体结构中心穿过，全方位拍取钢丝绳表面图片信息，为图像处理进而进行故障诊断和识别做准备；
图1和图2所示，所述励磁装置通过基座安装在机架上，可对钢丝绳进行励磁，以检测钢丝绳的有效金属截面积，进一步可得到钢丝绳的剩余强度(最大破断力)。
图1和图2所示，所述的轴承信号采集系统包括隔音箱、噪声传感器、加速度传感器和天轮，隔音箱安装固定在机架顶层端头，天轮安装在隔音箱内部，噪声传感器安装在隔音箱内部，且其底部固定在机架上，加速度传感器安装在可更换故障轴承的外侧轴承座上，钢丝绳穿过隔离箱并缠绕在天轮上；轴承信号采集系统在进行轴承的噪声和振动信号采集过程中，模拟正常和故障轴承在旋转运行状态下的噪声和振动信号变化情况；同时第三热成像仪安装在轴承信号采集系统的隔音箱内，用于测量轴承在故障状态下由磨损引起的温度变化。
图1和图2所示，所述的张力传感器安装在机架上，如图2所示的I位置处，增加安装一个张力传感器时，可测轴承信号采集系统中天轮上的张力大小，如图2所示的I和II位置处，分别增加安装一个张力传感器时，可测量摩擦导向轮上的张力大小；
作为本发明的进一步优选，横轴通过键和螺钉固定在轴承座上；摩擦导向轮通过键和螺钉安装固定在横轴中央；
作为本发明的进一步优选，在摩擦导向轮的一侧安装有用于测量摩擦导向轮在钢丝绳故 障状态下表面材料与钢丝绳摩擦磨损温度变化的第一热成像仪；
作为本发明的进一步优选，上衬垫通过螺纹孔固定安装在压紧缸的活塞杆上；在摩擦磨损滑座下方安装有固定在机架上的直线导轨，使摩擦磨损滑座在机架上移动；
作为本发明的进一步优选，前述的摩擦磨损装置还设有用于监测在磨损过程中摩擦磨损状况与温度变化间关系的第二热成像仪；
作为本发明的进一步优选，前述的钢丝绳图像采集系统包括三个CCD工业相机，以内六角中心为圆心，三个CCD工业相机两两呈120度安装均匀分布安装在内六角体内；
作为本发明的进一步优选，钢丝绳穿过隔离箱并缠绕在天轮上，前述的天轮表面含有摩擦材料；在轴承信号采集系统的隔音箱内安装第三热成像仪；
如图6所示，通过上述健康监测装置，可以对系统的关键零部件进行多状态健康监测，如：初始设置、故障状态模拟、故障信息采集与处理、基于深度学习方法的并行计算、故障识别及分类等等；具体实施方式如下：
实施例一：通过提升系统关键部件多状态健康监测装置对钢丝绳进行监测的方法，首先安装含锈蚀故障的钢丝绳，将含锈蚀故障的钢丝绳依次穿过大带轮、摩擦磨损装置、制损装置、张紧装置、内六角体中心位置处、励磁装置、张力传感器以及天轮，形成完整回路；先第一次启动电机，使整个健康监测装置进行缓慢匀速运行，使钢丝绳的锈蚀部分运行到指定位置即避开摩擦磨损装置和制损装置的任意位置；关闭电机，调节压紧缸和动作缸的压力，并设置动作缸的活塞运动范围，启动摩擦磨损装置，上称垫在压紧缸的作用力压紧钢丝绳，动作缸往复动作一定时间，使钢丝绳表面产生一定的磨损故障；关闭并复位摩擦磨损装置，启动电机，使任意段的健康钢丝绳运行到制损装置处；关闭电机，调节制损缸的压力，并设置冲头安装角度，启动制损装置，在钢丝绳上冲压出不同角度及深度的凹痕故障；关闭并复位制损装置，启动电机，使任意段的健康钢丝绳运行到制损装置处；关闭电机，更换冲头类型(此类型冲头头部相比于制造凹痕的冲头头部更加尖锐)，并使其角度与钢丝绳轴线垂直，调节制损缸的压力，启动制损装置，结合人工方式制造不同根数和位置的断丝故障；当钢丝绳上同时拥有锈蚀、磨损、凹痕、断丝故障后二次启动电机，使整个监测装置再次进行缓慢匀速运行，此时开启钢丝绳图像采集系统，通过三个CCD工业相机进行多角度提取图片数据信息；钢丝绳图像采集系统将采集到的图片传入上位机，对图像预处理后输入事先训练好的CNN深度神经网络模型中，上位机对数据进行并行计算，实现图像中故障的快速识别和分类；最后输出结果，得到钢丝绳的故障类型；
实施例二：通过提升系统关键部件多状态健康监测装置对轴承进行监测的方法，首先在 轴承信号采集系统的外侧轴承座上安装正常或者故障的轴承(多种故障轴承可选择，模拟不同故障，包括内圈、滚动体、保持架和内圈故障)；将健康的钢丝绳依次穿过大带轮、摩擦磨损装置、制损装置、张紧装置、内六角体中心位置处、励磁装置、张力传感器以及天轮，形成完整回路，同时调整张紧缸的张紧力(多种张紧力可选择，模拟不同负载下钢丝绳的张力)；启动电机，使整个健康监测装置匀速运行(多种速度可选，模拟不同运行速度)，通过第三热成像仪与加速度传感器共同配合提取噪声和温度数据信息；将采集到的噪声和温度数据信息传入上位机，对噪声信号预处理后输入事先训练完毕的RNN深度神经网络模型中，上位机对数据进行并行计算，实现轴承噪声信号中故障特征的快速识别和分类；最后结果输出，得到轴承的故障类型。
实施例三：通过提升系统关键部件多状态健康监测装置对轴承进行监测的方法，首先在轴承信号采集系统的外侧轴承座上安装正常或者故障的轴承(多种故障轴承可选择，模拟不同故障，包括内圈、滚动体、保持架和内圈故障)；将健康的钢丝绳依次穿过大带轮、摩擦磨损装置、制损装置、张紧装置、内六角体中心位置处、励磁装置、张力传感器以及天轮，形成完整回路，同时调整张紧缸的张紧力(多种张紧力可选择，模拟不同负载下钢丝绳的张力)；启动电机，使整个健康监测装置匀速运行(多种速度可选，模拟不同运行速度)，通过第三热成像仪与加速度传感器共同配合提取轴承座振动信号和温度数据信息；将采集到的轴承座振动信号传入上位机，对振动信号预处理后输入事先训练好的DBN深度神经网络模型中，上位机对数据进行并行计算，实现振动信号中故障特征的快速识别和分类；最后结果输出，得到轴承的故障类型。
如图7所示，上位机中基于深度学习的故障诊断方法，以钢丝绳表面图像故障诊断为例：首先，进行已知工况下钢丝绳表面图像故障信息的深度学习，以得到训练样本重构信号；然后，进行待测工况下钢丝绳表面图像故障信息的深度学习，以得到测试样本重构信号；最后，采用分类器(如支持向量机、浅层神经网络等)对测试样本重构信号与训练样本重构信号进行匹配，根据重构信号相似度进行故障识别与分类，从而得到故障诊断结果。深度学习过程中采用GPU并行加速计算，以提高计算效率。
进一步的，如图6和7所示，一种提升系统关键部件多状态健康监测方法，通过CNN深度神经网络模型完成钢丝绳表面的故障类型判断后，结合励磁装置测得的故障处的剩余强度和张力传感器测得的故障处的实时张力(故障处两侧张力传感器差值)，可求得钢丝绳的动态安全系数，以全绳最小安全系数作为其安全性评价标准，与规定的安全系数进行比较，从而对钢丝绳的安全性作出评价：当全绳最小安全系数不小于规定的安全系数时，钢丝绳处于健 康状况中。
动态安全系数：
Figure PCTCN2017118989-appb-000001
式中，F Ri为第i处钢丝绳故障处的剩余强度，前述的剩余强度是通过励磁装置对钢丝绳进行励磁，以检测钢丝绳的有效金属截面积，进一步得到钢丝绳的最大破断力，这个破断力就是剩余强度，F i为第i处钢丝绳故障处的实测张力，以全绳最小安全系数作为其安全性评价标准：
Figure PCTCN2017118989-appb-000002
式中，min{F Ri}为全钢丝绳最小剩余强度，max{F i}为全绳最大的实测张力，[k a]为规程规定的安全系数，其值为设计破断力总和与最大静载荷的比值，根据这个公式，找出i处中最小剩余强度和最大实测张力，它们的比值若大于或等于规程规定的安全系数，则说明钢丝绳是健康的。
如图6和7所示，一种提升系统关键部件多状态健康监测方法，通过RNN和DBN深度神经网络模型分别完成轴承故障类型的判断，可对诊断过程及结果进行对比分析，以为实际工程中提升系统旋转机械健康监测方法及深度神经网络模型的选择提供依据。
如图1至图7所示，一种提升系统关键零部件多状态健康监测装置及方法，还可用以研究以下参数之间的关系：钢丝绳故障状况及故障程度与摩擦温度、张力、剩余强度、天轮接触应力等参数间的相互关系；轴承故障状况及故障程度与磨损温度、噪声、径向载荷、转速等参数间的相互关系等。上述参数间的关系对钢丝绳及轴承健康状态的评估起到了一定的指导作用，可在明确了故障类型的基础上判断故障程度的大小。
本技术领域技术人员可以理解，除非另外定义，这里使用的所有术语(包括技术术语和科学术语)具有与本申请所属领域中的普通技术人员的一般理解相同的意义。还应该理解的是，诸如通用字典中定义的那些术语应该被理解为具有与现有技术的上下文中的意义一致的意义，并且除非像这里一样定义，不会用理想化或过于正式的含义来解释。
本申请中所述的“和/或”的含义指的是各自单独存在或两者同时存在的情况均包括在内。
本申请中所述的“连接”的含义可以是部件之间的直接连接也可以是部件间通过其它部件的间接连接。
以上述依据本发明的理想实施例为启示，通过上述的说明内容，相关工作人员完全可以 在不偏离本项发明技术思想的范围内，进行多样的变更以及修改。本项发明的技术性范围并不局限于说明书上的内容，必须要根据权利要求范围来确定其技术性范围。

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  1. 一种提升系统关键部件多状态健康监测装置，其特征在于：包括机架，所述的机架由多个矩形钢焊接形成方形结构，围绕方形结构外围布设有钢丝绳，在方形结构的底层由左向右依次安装动力系统、摩擦磨损装置、制损装置和张紧装置，在方形结构的顶层由左向右依次安装轴承信号采集系统、张力传感器、励磁装置和钢丝绳图像采集系统，钢丝绳顺次穿过前述所有装置或系统，在动力系统的带动下进行环绕运行，通过前述所有装置或系统实现钢丝绳运行状态的监测。
  2. 根据权利要求1所述的提升系统关键部件多状态健康监测装置，其特征在于：所述的动力系统包括电机、皮带、小带轮、大带轮和摩擦导向轮组件，前述的摩擦导向轮组件包括摩擦导向轮、横轴和轴承座，其中，电机安装在机架底层端部处，轴承座与电机相邻安装在机架上，横轴固定在轴承座上，大带轮安装固定在横轴端头，摩擦导向轮安装固定在横轴中间部位，小带轮安装在电机的输出轴上，小带轮和大带轮通过皮带进行连接；
    所述的摩擦磨损装置包括压紧缸、动作缸、上衬垫、下衬垫和衬垫安装板，动作缸水平安装在机架底层上，在机架内部包括与机架底层相垂直的矩形钢，矩形钢上水平安装压紧缸，其中，上衬垫固定安装在压紧缸的活塞杆上，在机架上还安装可移动的摩擦磨损滑座，摩擦磨损滑座位于压紧缸下方，摩擦磨损滑座上安装有衬垫安装板，下衬垫安装在衬垫安装板上；
    所述的制损装置包括制损缸、制损头和支撑衬垫，制损缸水平安装在与机架底层相垂直的矩形钢上，制损头可活动安装在制损缸的活塞杆上，支撑衬垫水平固定在位于制损头正下方的机架底层上；
    所述的张紧装置包括张紧缸、拉压传感器、平移板、安装架、直线导轨和张紧滑座，张紧滑座可滑动安装在机架底层上，在张紧滑座的下方同时设有固定在机架底层上的直线导轨，张紧滑座上固定安装架，平移板安装在安装架上，前述的张紧缸与安装架相邻固定安装在机架底层上，拉压传感器的一端与张紧缸的活塞杆连接，其另一端与平移板相连接；在平移板表面设有导向轮组件，钢丝绳穿过导向轮组件，在张紧缸作用下实现张紧与放松动作；
    所述的钢丝绳图像采集系统包括相机安装架和多个CCD工业相机，所述的相机安装架为圆柱体结构，其内固定安装内六角体，多个CCD工业相机均匀分布安装在内六角体内，前述的钢丝绳由圆柱体结构中心穿过；
    所述的轴承信号采集系统包括隔音箱、噪声传感器、加速度传感器和天轮，隔音箱安装固定在机架顶层端头，天轮安装在隔音箱内部，噪声传感器安装在隔音箱内部，且其底部固定在机架上，加速度传感器安装在可更换故障轴承的外侧轴承座上，钢丝绳穿过隔离箱并缠绕在天轮上。
  3. 根据权利要求1所述的提升系统关键部件多状态健康监测装置，其特征在于：横轴通过键和螺钉固定在轴承座上；摩擦导向轮通过键和螺钉安装固定在横轴中央。
  4. 根据权利要求1所述的提升系统关键部件多状态健康监测装置，其特征在于：在摩擦导向轮的一侧安装有用于测量摩擦导向轮在钢丝绳故障状态下表面材料与钢丝绳摩擦磨损温度变化的第一热成像仪。
  5. 根据权利要求1所述的提升系统关键部件多状态健康监测装置，其特征在于：上衬垫通过螺纹孔固定安装在压紧缸的活塞杆上；在摩擦磨损滑座下方安装有固定在机架上的直线导轨，使摩擦磨损滑座在机架上移动。
  6. 根据权利要求1所述的提升系统关键部件多状态健康监测装置，其特征在于：前述的摩擦磨损装置还设有用于监测在磨损过程中摩擦磨损状况与温度变化间关系的第二热成像仪。
  7. 根据权利要求1所述的提升系统关键部件多状态健康监测装置，其特征在于：前述的钢丝绳图像采集系统包括三个CCD工业相机，以内六角中心为圆心，三个CCD工业相机两两呈120度安装均匀分布安装在内六角体内。
  8. 根据权利要求1所述的提升系统关键部件多状态健康监测装置，其特征在于：钢丝绳穿过隔离箱并缠绕在天轮上，前述的天轮表面含有摩擦材料；在轴承信号采集系统的隔音箱内安装第三热成像仪。
  9. 一种通过提升系统关键部件多状态健康监测装置对钢丝绳进行监测的方法，其特征在于：首先安装含锈蚀故障的钢丝绳，将含锈蚀故障的钢丝绳依次穿过大带轮、摩擦磨损装置、制损装置、张紧装置、内六角体中心位置处、励磁装置、张力传感器以及天轮，形成完整回路；先第一次启动电机，使整个健康监测装置进行缓慢匀速运行，使钢丝绳的锈蚀部分运行到与摩擦磨损装置和制损装置的不接触处；关闭电机，启动摩擦磨损装置和制损装置，在钢丝绳上分别制造磨损、凹痕以及断丝故障，并且使各种故障等间距分开；二次启动电机，使整个监测装置再次进行缓慢匀速运行，此时开启钢丝绳图像采集系统，通过三个CCD工业相机进行多角度提取图片数据信息；钢丝绳图像采集系统将采集到的图片传入上位机，对图像预处理后输入事先训练好的CNN深度神经网络模型中，上位机对数据进行并行计算，实现图像中故障的快速识别和分类；最后输出结果，得到钢丝绳的故障类型。
  10. 一种通过提升系统关键部件多状态健康监测装置对轴承进行监测的方法，其特征在于：首先在轴承信号采集系统的外侧轴承座上安装正常或者故障的轴承；将健康的钢丝绳依次穿过大带轮、摩擦磨损装置、制损装置、张紧装置、内六角体中心位置处、励磁装置、张力传感器以及天轮，形成完整回路，同时调整张紧缸的张紧力；启动电机，使整个健康监测装置 匀速运行，通过第三热成像仪与加速度传感器共同配合提取噪声和温度数据信息；将采集到的噪声和温度数据信息传入上位机，对噪声信号预处理后输入事先训练完毕的RNN深度神经网络模型中，上位机对数据进行并行计算，实现轴承噪声信号中故障特征的快速识别和分类；最后结果输出，得到轴承的故障类型。
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