Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
  • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
  • G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
  • G01N29/4436—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with a reference signal
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
  • G01M7/00—Vibration-testing of structures; Shock-testing of structures
  • G01M7/02—Vibration-testing by means of a shake table
  • G01M7/025—Measuring arrangements
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
  • G01N29/04—Analysing solids
  • G01N29/043—Analysing solids in the interior, e.g. by shear waves

Definitions

• TITLE METHOD AND SYSTEM FOR DETECTING ANOMALIES IN A STRUCTURE
• the present invention relates to a method for detecting anomaly(s) in a structure and for SHM health monitoring of said structure, an anomaly corresponding to modifications in the physical and/or geometric properties of the structure, said structure carrying at least one sensor for measuring at least one characteristic of said structure, the method comprising at least one learning phase and at least one operational phase.
• the invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a method of detecting anomaly(s) in a structure.
• the invention also relates to an electronic device for detecting anomaly(s) in a structure, said structure carrying at least one sensor configured to generate and receive guided waves, the device comprising a learning unit and a test unit.
• the invention also relates to a system for detecting anomaly(s) in a structure.
• the present invention relates to the field of structural health monitoring or SHM (Structural Health Monitoring) aimed at detecting and characterizing damage (i.e. anomalies) of structures/infrastructure.
• SHM Structuretural Health Monitoring
• Such structural anomalies correspond to modifications of the physical and/or geometric properties of the structure considered likely to affect its performance and/or reliability.
• Such structural health monitoring is implemented using integrated sensors, for example piezoelectric sensors capable of emitting and receiving ultrasonic guided elastic waves.
• sensors on or within structures/infrastructure to monitor their condition makes it possible in particular to guarantee the safety of supporting structures corresponding to thin and/or long mechanical components, such as wind turbine blades, fuselages or components. of aircraft engines, metal or composite pipes, tension cables, bridge suspension cables, train rails, etc.
• the guided elastic ultrasonic waves emitted by such sensors make it possible to detect structural defects leading to discontinuities or variations in geometry, such as cracks, delamination in composite fuselages, corrosion leading in particular to loss of thickness in metals, etc., at an early stage and thus monitor them for decades.
• Such detection is generally well mastered in the laboratory where external effects are limited, controlled and calibrated.
• the main challenge associated with such a structural diagnosis is, in real conditions, linked on the one hand to the presence of epistemic uncertainties on the structure or instrumentation such as the positions of the sensors, the properties of the sensors, the properties elastic or geometric of the structure, and on the other hand linked to the presence of unsupervised external influence effects (i.e. unknown) evolving with distinct temporal dynamics.
• Such unsupervised external influence effects can be classified on the one hand into so-called “rapid” effects, presenting a variation on a time scale ranging from the hour to the day, such as the variation of temperature, humidity and stress (i.e. constraints exerted on a material), and on the other hand so-called “slow” effects, presenting a variation on a time scale of the order of months, years, etc., such such as the aging of the sensors, the modification of the material properties, the structure or the coupling between sensors and structure, and due to thermomechanical or other cycles.
• a classic solution aims to compensate for epistemic uncertainties by means of a reference state, that is to say a measurement in the absence of a defect then by comparing the current state to the reference state, in particular by subtraction , correlation, etc. while assuming that the only difference between reference and current states can only be attributed to the presence of a fault, but that all other usage parameters, apart from at best a single rapid effect parameter such as the temperature, are equal between these two reference and current states.
• Temperature compensation is also limited in terms of the maximum compensable range which is currently of the order of a maximum temperature difference of 15°.
• the classic solutions do not take into account the aforementioned slow effects, and are implemented by assuming that the system behaves nominally over long periods of time, which is of course not satisfactory when we discuss the instrumentation of a structure over several decades during which the aging of the measurement sensor and its coupling to the structure is inevitable and generates a slow drift in the signals received by the measurement sensor, likely to be wrongly interpreted as a defect, or to hide one.
• the aim of this invention is therefore to propose a method and a device capable of providing an early and reliable diagnosis of structural defect in real conditions of use in the presence of epistemic uncertainties and unsupervised external effects at the same time with rapid dynamics. (i.e. on the order of an hour to several days) and slow (i.e. on the order of a month to several years).
• the subject of the invention is a method for detecting anomaly(s) in a structure and for monitoring the SHM health of said structure, an anomaly corresponding to modifications in the physical and/or geometric properties of the structure, said structure carrying at least one sensor configured to generate and receive guided waves, the method comprising at least one learning phase and at least one operational phase, the learning phase comprising the following steps:
• N an integer
• two distinct sets presenting at least one condition distinct use from one set to another, said obtaining of a set of healthy data being implemented via a plurality of Q measurement sensors carried by said structure forming a network of sensors, Q being an integer greater than one, at least one of said Q sensors being configured to generate and receive guided ultrasonic elastic waves;
• This method thus aims to quantify to what extent signal data, corrupted on various time scales, can be used for the analysis of structural states. Such a process thus makes it possible to compensate for the influence of the aforementioned supervised and/or unsupervised external effects without erasing the signature of the small defects that we seek to detect.
• the invention consists of obtaining all the signal data capable of being measured in real healthy conditions, said data subsequently being called healthy data, that is to say in the absence of a fault, with the aim of detecting a defect by its absence from this set.
• obtaining amounts to building a digital twin describing all the healthy signals to then detect an anomaly beyond the contour of the “healthy” space described by this twin.
• Such a “healthy” space obtained according to the present invention is an important and differentiating element compared to conventional solutions. Indeed, according to the present invention, we do not seek to model defects, because these will be detected by their absence from this set of healthy data. Not modeling defects is a significant advantage because it means not making assumptions related to the typology, position or size of the defects.
• the present invention further proposes to go through a dimensionality reduction, via a latent space, to describe the characteristics of the signals in a healthy state.
• the method for detecting anomaly(s) in a structure comprises one or more of the following characteristics, taken in isolation or in all technically possible combinations:
• said set of healthy data is obtained following a preliminary phase of calibration of said structure for the N distinct sets in pairs of conditions of use of said structure;
• said set of healthy data is obtained following a preliminary phase of simulation of said structure for the N distinct sets in pairs of conditions of use of said structure;
• said set of healthy data is obtained following a preliminary hybrid phase of calibration and/or simulation of said structure for the N sets, distinct in pairs, of conditions of use of said structure;
• the method comprises, during said hybrid preliminary phase, a step of compensation by transfer learning in the event of a discrepancy between calibration and simulation for the same set of conditions of use of said structure;
• said latent space of reduced dimension is obtained by supervised or unsupervised dimensional reduction
• said unsupervised dimensional reduction is implemented by means of one of the elements belonging to the group comprising at least:
• an autoencoder previously trained to compress and then decompress the signals from the healthy data set, and of which only the part dedicated to compression is used to implement said dimensional reduction.
• said supervised dimensional reduction is implemented by means of a neural network
• said neural network is a neural network whose type belongs to the group comprising:
• said contour determination consists of:
• the invention also relates to a computer program comprising software instructions which, when executed by a computer, implement a method for detecting anomaly(s) in a structure as defined above.
• the invention also relates to a system for detecting anomaly(s) in a structure and for monitoring the SHM health of said structure, an anomaly corresponding to modifications in the physical and/or geometric properties of the structure, said system comprising a plurality of Q sensors measuring at least one characteristic of said structure carried by said structure and forming a network of sensors, Q being an integer greater than one, at least one of said Q sensors being configured to generate and receive elastic waves guided ultrasound, said system comprising a device in said structure and comprising a learning unit and a test unit, the learning unit comprising:
• a first obtaining module configured to obtain, via said plurality of Q measurement sensors, a set of healthy data representative of N healthy states of said structure respectively associated with N sets, distinct in pairs, of conditions of use of said structure, N being an integer, two distinct sets presenting at least one condition of use distinct from one set to another;
• a first projection module configured to project said set of healthy data into a latent space of reduced dimension relative to the dimension of said set of healthy data
• test unit configured to determine a contour of said set of healthy data projected into said latent space;
• a second obtaining module configured to obtain, by measurement via said at least one sensor, a set of test data representative of the current state of said structure
• a second projection module configured to project said set of test data into said latent space provided by the projection module of said learning unit
• Figure 1 illustrates the effect of a structural defect on the propagation of guided waves on the surface of a structure.
• Figure 2 is a schematic view of an electronic device for detecting anomaly(s) in a structure according to the present invention
• Figure 3 is a flowchart of a method for detecting anomaly(s) in a structure according to the present invention
• FIG 4 [Fig 5] Figures 4 and 5 respectively illustrate the learning phase and the operational phase of the method according to the present invention.
• FIG 1 an example of propagation of guided waves on the surface of a structure 10 is illustrated for the structure without a defect on view A and in the presence of a defect on view B.
• the structure 10 free of defects corresponds to a cylindrical pipe free of defects, of length L, and carrying on the surface at least one measurement sensor in an uncontrolled environment, for example configured to generate, via element 12 and receive via element 14 guided waves 16, in particular elastic and ultrasonic.
• a sensor belongs to the group comprising at least: piezoelectric sensors, an electro-magneto-acoustic transducer EMAT (from the English Electro magneto acoustic transducer), a polyvinylidene fluoride sensor PVDF (from the English Polyvinylidene fluoride) , etc.
• EMAT electro-magneto-acoustic transducer
• PVDF from the English Polyvinylidene fluoride
• sensors are suitable for use as long as they are capable of carrying out a measurement in an uncontrolled environment, for example sensors based on the use of ultrasound in general, including as mentioned above. above sensors capable of generating and receiving guided waves, but also sensors for measuring eddy currents or vibration measurement, etc.
• sensors capable of generating and receiving guided waves, but also sensors for measuring eddy currents or vibration measurement, etc.
• the presence of a defect 18 on the surface of the pipe modifies the propagation of the guided waves by generating in particular reflected guided waves 20.
• FIG. 2 is a schematic view of an electronic device 30 for detecting anomaly(s) in a structure according to the present invention.
• a device 30 comprises an automatic learning unit 32 comprising a first obtaining module 34 configured to obtain a set of healthy data representative of N healthy states of said structure respectively associated with N sets, distinct in pairs, of conditions of use of said structure, N being an integer, two distinct sets presenting at least one condition of use distinct from one set to another.
• the learning unit 32 further comprises a first projection module 36 configured to project said set of healthy data (also called healthy base comprising N samples, each sample being of dimension P) into a latent space E of reduced dimension relative to to the size of said healthy data set.
• a first projection module 36 configured to project said set of healthy data (also called healthy base comprising N samples, each sample being of dimension P) into a latent space E of reduced dimension relative to to the size of said healthy data set.
• such a latent space E is configured to describe the aforementioned disruptive effects as simply as possible, and is ideally linear, if not monotonic.
• the learning unit 32 also includes a determination module 38 configured to determine a contour C, in particular multidimensional, of said set of healthy data projected into said latent space.
• the electronic device 30 comprises a test unit 40, capable of receiving as input the output S of the learning unit 32, and comprising a second obtaining module 42 configured to obtain, by measurement via said at least a sensor, a set of test data representative of the current state of said structure 10.
• the test unit 40 further comprises a second projection module 44 configured to project said set of test data into said latent space provided by the first projection module 36 of said learning unit 32.
• the test unit 40 also comprises a detection module 46 configured to detect at least one current anomaly of said structure as soon as an element of said set of test data is outside of said contour provided by said module 38 for determining said unit. learning 32.
• the learning unit 32 is capable of constructing by training a digital twin describing all of the healthy data to detect an anomaly beyond the healthy space described by this digital twin.
• the electronic device 30 for detecting anomaly(s) in a structure comprises an information processing unit 50 formed for example of a memory 52 and a processor 54 associated with the memory 52.
• the first obtaining module 34, the first projection module 36, the determination module 38, the second obtaining module 42, the second projection module 44 and the detection module 46 are each produced under form of a programmable logic component, such as an FPGA (Field Programmable Gate Array), or a GPU graphics processor (Graphics Processing Unit), or even in the form of an integrated circuit, such as an ASIC (Application Specific Integrated Circuit).
• a programmable logic component such as an FPGA (Field Programmable Gate Array), or a GPU graphics processor (Graphics Processing Unit), or even in the form of an integrated circuit, such as an ASIC (Application Specific Integrated Circuit).
• the information is therefore, in this case, processed at the sensor network level by exploiting Q 2 -Q signals corresponding to the paths between the Q sensors, or Q 2 signals if we exploit the signals transmitted and received by the same sensor.
• the network of Q sensors is where appropriate supplemented by an acquisition chain not shown and configured to process the measurements captured via said network of Q sensors. Indeed, it is not excluded that a signal acquired by a sensor element 14 as illustrated in FIG. 1, in the presence of a fault under conditions of use Ui, is identical to a signal acquired in l 'absence of fault under Us conditions- To remove this ambiguity, the present invention then proposes to be based on data from a network of sensors.
• the contribution of a given defect is variable depending on the paths traveled by the waves, which is not the case for operational conditions.
• the data is therefore processed at the sensor network level, Q 2 -Q signals corresponding to the paths between the Q sensors.
• Figure 3 schematically illustrates an example of implementation, according to the present invention, of a method 60 for detecting anomaly(s) in a structure, such as for example structure 10 of Figure 1.
• the method 60 firstly comprises an automatic learning phase 62, also illustrated in more detail in Figure 4 described subsequently.
• the electronic device 30 for detecting anomaly(s), via its first obtaining module 34 obtains OBT A a set of healthy data representative of N healthy states of said structure respectively associated with N sets, distinct in pairs, of conditions of use of said structure, N being an integer, two distinct sets presenting at least one condition of use distinct from one set to another.
• the N sets, distinct in pairs, of conditions of use of said structure aim in particular to cover the main operating parameters likely to influence the measurements, namely those associated with the aforementioned epistemic effects, such as the placement of sensors, the properties and geometry of the structure, the properties of the sensors, those associated with the aforementioned rapid effects, such as temperature, humidity, pressure, the forces applied to the structure considered, those associated with slow effects such as the aging of the or sensors, the structure and the coupling between the structure and the sensor(s).
• epistemic effects such as the placement of sensors, the properties and geometry of the structure, the properties of the sensors, those associated with the aforementioned rapid effects, such as temperature, humidity, pressure, the forces applied to the structure considered, those associated with slow effects such as the aging of the or sensors, the structure and the coupling between the structure and the sensor(s).
• such obtaining 64 follows one of the three optional preliminary phases 642, 644, 646, implemented by said electronic device 30 for detecting anomaly(s) or by a separate device, the first optional preliminary phase 642 corresponding to a preliminary phase of CAL calibration of said structure for the N distinct sets in pairs of conditions of use of said structure.
• the set of healthy data is determined beforehand by experience during a calibration phase 642, often likely to be prohibitive in terms of costs/duration, and consisting of instrumenting structures healthy and acquire data over long periods of time.
• Such a calibration phase 642 involving both the supervision of the measured data to ensure that there are no defects, and their completeness in order to ensure that a statistically sufficient number of combinations of parameters are explored. .
• the obtaining step 64 then consisting of recovering the set of healthy data resulting from the CAL calibration phase 642.
• step d obtaining 64 then consists of recovering the set of healthy data resulting from the SIM simulation phase 644.
• This second option is a preferred approach, according to the present invention, the prior simulation 644 making it possible to establish, quickly and at lower cost than the first experimental option, models (i.e. simulations) of the signals subject to the disturbing parameters corresponding to the epistemic uncertainties and to the aforementioned unsupervised external effects, and thus generate healthy data on the fly.
• models i.e. simulations
• Such a second option by simulation also implies having available models of all the phenomena influencing the guided waves, such models being suitable for being learned/calibrated/tested/validated on samples representative of the structure and the application considered, greatly limiting the cost compared to the experimental solution associated with the first option mentioned above.
• models (i.e. simulations) of defects are not necessary, because the present invention proposes to focus on healthy data
• said set of healthy data is obtained following a preliminary hybrid phase 646 of calibration and/or simulation of said structure for the N sets, distinct in pairs, of conditions of use of said structure, the stage of obtaining 64 then consisting of recovering the set of healthy data resulting from the hybrid phase 646 HYB.
• said hybrid preliminary phase includes a step, not shown, of compensation by transfer learning (from the English transfer learning) in the event of a discrepancy between calibration and simulation for the same set of conditions of use of said structure.
• transfer learning from the English transfer learning
• said hybrid preliminary phase includes a step, not shown, of compensation by transfer learning (from the English transfer learning) in the event of a discrepancy between calibration and simulation for the same set of conditions of use of said structure.
• transfer learning from the English transfer learning
• the network of Q sensors makes it possible to ensure that the set of data recovered during the obtaining step 64 is healthy.
• the external effects are relatively homogeneous and perceived by all the sensors. This is the case, for example, of temperature, which can affect all measurement sensors in a relatively homogeneous manner.
• a defect will only influence certain sensors and in a quite different way, due to its limited size and its natural asymmetry.
• the learning phase 62 of the method 60 according to the present invention further comprises a step 66 of projection PA of said set of healthy data into a latent space E of reduced dimension compared to the dimension of said set of healthy data.
• the set of healthy data is generally large, in particular of the order of 10 3 to 10 5 , which is likely to slow down anomaly detection during the operational test phase.
• the projection step 66 therefore aims to reduce the dimensions of the healthy learning data, by describing them (ie projecting) into a latent space of dimension reduced, for example of the order of 10 to 10 2 for original dimensions respectively of 10 3 to 10 5 .
• Said latent space of reduced dimension is obtained by supervised or unsupervised dimensional reduction
• an unsupervised dimensional reduction 66 makes it possible not to make any a priori unfavorable to the detection of defects.
• an unsupervised dimensional reduction 66 can be implemented to avoid creating a bias going against the signature of defects, absent from the initial set of healthy data.
• the dimensional reduction 66 when the dimensional reduction 66 is unsupervised, it is implemented by means of one of the elements belonging to the group comprising at least:
• an autoencoder previously trained to compress and then decompress the signals from the healthy data set, and of which only the part dedicated to compression is used to implement said dimensional reduction.
• the first projection module 36 is suitable for implementing a principal component analysis by ensuring in particular that the loss of variance is less than the influence of a defect subsequently sought during the operational phase.
• the first projection module 36 is configured to drive the first half of said autoencoder to compress/decompress the signals, and the dimensional reduction is obtained by retaining only the central component of the autoencoder commonly called “embedding”.
• a dimensional reduction 66 supervised is implemented by means of a neural network, the type of which belongs for example to the group comprising:
• Such a neural network is particularly suitable for modeling data and disturbing effects such as temperature, humidity, etc., the latent space of reduced dimension then corresponding to all of the outputs of the neural network.
• the learning phase 62 of the method 60 according to the present invention further comprises a step 68 of determining the contour C, implemented automatically via said determination module 38 or manually, of said set of healthy data projected into said latent space E.
• a step 68 of determining the contour C implemented automatically via said determination module 38 or manually, of said set of healthy data projected into said latent space E.
• the learning phase 62 is capable of being repeated in particular to take into account additional parameters capable of influencing the propagation guided waves depending on the application of the desired structure, for example in the event of a change in the climatic zone of operation of said structure.
• the present invention makes it possible to take into account the knowledge of a physical parameter of condition of use at the time of measurement 72, such a physical parameter corresponding in particular to the temperature, to reduce the set of healthy data resulting from learning 62 to a set of healthy data restricted by one dimension due to this known physical parameter.
• the learning 62 is repeated to obtain said set of restricted healthy data, and its contour C' in a latent space E' associated with this dimension reduction linked to perfect knowledge of a use parameter such as the temperature during measurement 72.
• a contour C' is necessarily more restricted than the contour C associated with the set of healthy data obtained for a plurality of distinct operating temperatures of the structure.
• Such a more restricted C' contour makes the approach more reliable, and in the extreme case where all the influential parameters are known, which does not seem possible in practice, due to epistemic uncertainties, the set of healthy data would then contain a single dataset.
• Such an operational phase 70 comprises a first step 74 of obtaining OBT_T, via the second obtaining module 42 of Figure 2, of a set of test data representative of the current state of said structure considered, in which the presence of a defect is unknown.
• step 76 implemented by the second projection module 44 of Figure 2, the set of test data representative of the current state of said structure considered is projected, via a P_T projection, into the latent space E, used during the learning phase 62.
• a step 78 implemented automatically by the detection module 46 of FIG. 2 or manually, at least one current anomaly of said structure is detected as soon as an element of said set of test data is outside of said contour C.
• the method 60 is also capable of comprising an evaluation of biases and errors due to modeling and machine learning in order to provide a measure of confidence in the fault diagnosis (i.e. anomaly detection ) proposed according to the present invention.
• Figure 4 illustrates, schematically, in more detail the learning phase 62 previously described, with step 64 of obtaining a set of healthy data representative of N healthy states of said structure respectively associated with N sets, distinct in pairs, of conditions of use of said structure, N being an integer, two distinct sets presenting at least one condition of use distinct from one set to another.
• each of the N signals represented is obtained with a variation of at least one use parameter such as the position of at least one sensor, the temperature and/or the aging of said at least one sensor.
• Step 66 of projecting said set of healthy data into a latent space of reduced dimension M relative to the dimension N of said set of healthy data is illustrated in Figure 4 by M 2 two-dimensional representations of the healthy data represented by crosses.
• Step 68 of determining the contour of said set of healthy data (also called healthy base comprising N samples, each sample being of dimension P) projected into said latent space is illustrated in Figure 4 by the contours Ci and C2, the contour Ci surrounding the healthy data crosses projected during step 66 in a two-dimensional part with dimension 1 on the abscissa and dimension 2 on the ordinate of the latent space of M dimensions (i.e. of size M ⁇ P necessarily due to the reduction dimension), the contour C2 surrounding the healthy data crosses projected during step 66 in a two-dimensional part with dimension 1 on the abscissa and dimension 3 on the ordinate of the M-dimensional latent space.
• Figure 5 illustrates, schematically, in more detail the operational phase previously described, with step 74 of obtaining, by prior measurement via said at least one sensor, a set of test data representative of the current state of said structure.
• Step 76 of projecting said set of test data into said latent space, resulting from learning 62 illustrated by Figure 4, is represented in Figure 5 by M 2 two-dimensional representations of the test data T1 and T 2 represented by a cross in each two-dimensional representation.
• the anomaly detection step 78 compares, in each two-dimensional representation, that the test data cross is in the associated healthy data contours, otherwise an anomaly 82 is detected as soon as a cross is outside of a two-dimensional contour, which is the case for the cross T 1 which is outside the contour Ci in the two-dimensional space of dimensions 1 and 2, whereas when dimensions 1 and 3 are considered the cross T2 is in the contour C2.
• an anomaly is detected, and if necessary in a non-existent manner. represented, an alert not represented is raised and/or a request for maintenance of said structure.
• such detection 78 is certainly crude but effective, and consists of considering that if the representation in the latent space of the set of test data is too far from the contour C of the set of healthy data, a defect is here.
• the distance from the test data representative of an anomaly (here the cross T1) to the contour here Ci could be specific to reflecting the criticality of the detected defect.
• the present invention proposes a method and a device for detecting anomaly(s) in a structure which make it possible to construct, by training, a digital twin describing all the healthy data associated with a healthy state of the structure considered for then detect an anomaly as beyond the healthy space described by this digital twin.
• the present invention makes it possible to avoid the creation of defects, whether experimentally or by simulation, through learning by modeling only healthy states.
• the present invention is generic with respect to the influential parameters, because once the set of healthy data obtained by learning, it is possible to make it evolve quickly to take into account additional conditions of use of the structure and recalculate the multidimensional contour in the latent space.
• the anomaly detection obtained via the present invention presents an intrinsic robustness to external parameters by integrating them from the start in the learning of healthy data, so that the diagnosis (ie the detection) does not make a priori on the external conditions at the time of its application to the measurement of a current state of the structure.
• Such anomaly detection is capable of reducing the environmental footprint of instrumented structures thanks to preventive maintenance and extending the lifespan by detecting anomaly (i.e. fault) at an early stage.

Landscapes

• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Biochemistry (AREA)
• General Health & Medical Sciences (AREA)
• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Chemical & Material Sciences (AREA)
• Analytical Chemistry (AREA)
• Pathology (AREA)
• Immunology (AREA)
• Signal Processing (AREA)
• Engineering & Computer Science (AREA)
• Acoustics & Sound (AREA)
• Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
• Testing Of Short-Circuits, Discontinuities, Leakage, Or Incorrect Line Connections (AREA)
• Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=82694247

Family Applications (1)

Country Status (4)

Families Citing this family (1)

Family Cites Families (7)

• 2022
  • 2022-04-28 FR FR2204010A patent/FR3135142B1/fr active Active
• 2023
  • 2023-04-27 EP EP23722373.0A patent/EP4515224A1/de active Pending
  • 2023-04-27 US US18/859,407 patent/US20250258059A1/en not_active Abandoned
  • 2023-04-27 WO PCT/EP2023/061155 patent/WO2023209111A1/fr not_active Ceased

Also Published As

Similar Documents

Legal Events