WO2022077901A1 - Procédé de diagnostic de mode de défaillance de palier utilisant de petits ensembles de données d'échantillon, et système - Google Patents

Procédé de diagnostic de mode de défaillance de palier utilisant de petits ensembles de données d'échantillon, et système Download PDF

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WO2022077901A1
WO2022077901A1 PCT/CN2021/093451 CN2021093451W WO2022077901A1 WO 2022077901 A1 WO2022077901 A1 WO 2022077901A1 CN 2021093451 W CN2021093451 W CN 2021093451W WO 2022077901 A1 WO2022077901 A1 WO 2022077901A1
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bearing
matching
small sample
failure mode
dimensional
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Chinese (zh)
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徐高威
蒋卓甫
秦泰春
李鹏
刘敏
王子淳
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同济大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • 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
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing

Definitions

  • the invention relates to the technical field of fault diagnosis of high-end equipment structures, in particular to a bearing fault mode diagnosis method and system for small sample data sets.
  • rolling bearings As a key component in modern high-end equipment, rolling bearings have a weak ability to withstand shocks and are extremely prone to fatigue and damage. Once a failure occurs, it will have a huge negative impact on the entire production process, not only causing serious economic losses, but even endangering the lives of relevant personnel. Therefore, it is extremely necessary to carry out fault diagnosis technology research on rolling bearings, which is of great significance for the predictive maintenance of high-end equipment.
  • Meta-learning is mainly used to solve the problem of learning to learn. Different from previous machine learning and deep learning methods, meta-learning pays more attention to how to use known knowledge to quickly adapt to the learning of new tasks, so it can effectively solve the above two problems. In recent years, meta-learning has emerged and has played a significant role in solving the learning problem of a small number of labeled samples or even unlabeled sample data. However, the application of this method in the field of high-end equipment fault diagnosis is very lacking at present.
  • the purpose of the present invention is to provide a bearing failure mode diagnosis method and system for small sample data sets in order to overcome the above-mentioned defects of the prior art. In addition, it can also alleviate the performance degradation problem caused by the inconsistency of the sample distribution of the training set and the test set to a certain extent.
  • a bearing failure mode diagnosis method for small sample data set includes the following steps:
  • Step 1 Collect the vibration signal data of the bearing under different operating conditions of different equipment through the acceleration sensor, and store it in the server;
  • Step 2 Preprocess the signal in the server, convert the original one-dimensional signal into a two-dimensional signal through the continuous wavelet transform algorithm, and store it in the database in the form of an image;
  • Step 3 Construct a bearing fault diagnosis model framework based on convolutional neural network, including encoding module and matching module, and randomly sample from the image data in the database to construct a learning task of multiple small sample data sets to diagnose bearing faults model for training;
  • Step 4 Collect the vibration signal of the target bearing, diagnose it according to the preprocessing method in the step 2 and the bearing fault diagnosis model framework in the step 3, and obtain the bearing failure mode.
  • step 2 comprises the following sub-steps:
  • Step 201 The vibration data collected by the acceleration sensor is a one-dimensional continuous time series signal, and the signal is preprocessed by a continuous wavelet transform algorithm to obtain a two-dimensional signal;
  • Step 202 The two-dimensional signal is converted into image gray value and stored in the database in the form of image gray value conversion.
  • CWT f (a, b) is a two-dimensional signal
  • f(t) is a one-dimensional vibration signal
  • ⁇ (t) is the complex conjugate of the wavelet function ⁇ (t)
  • a and b represent the scaling and translation factors, respectively;
  • the two-dimensional signal is converted into an image gray value, and the description formula is as follows:
  • image(CWT f (a,b)) is the image data after the two-dimensional signal is converted into gray value
  • max( ⁇ ) is the maximum value function
  • min( ⁇ ) is the minimum value function
  • step 3 comprises the following sub-steps:
  • Step 301 randomly select l-type samples from all images as a small sample set for training, and construct a learning task
  • Step 302 Pass the sampling set and the query set in each learning task through the coding module to obtain a high-dimensional coding expression, and after averaging the coding values of the obtained sampling sets of the same type, perform the query set on the feature dimension. Splicing processing to form matching pairs;
  • Step 303 Pass each matching pair through the matching module to obtain a matching score
  • Step 304 use the mean variance quantification model to predict the error between the matching score and the actual matching score;
  • Step 305 Use the back-propagation algorithm in deep learning to optimize the model parameters until the final training is completed.
  • Task i is the learning task, is the sampling set, is the query set, m is the number of samples in the sampling set, n is the number of samples in the query set, k and j are both natural numbers;
  • Pair(l,k) is the matching pair
  • Cat( ) is the splicing function in the feature dimension
  • M i,l is the average value of the coded value of the l-th sample sample in the ith task
  • r l,k ⁇ [0,1] is the matching score between the kth query set and the lth class
  • g ⁇ ( ) is the function map of the matching module
  • the matching score predicted by the model in the step 304 and the actual matching score error is:
  • Loss is the error between the matching score predicted by the model and the actual matching score
  • the back-propagation algorithm in deep learning is used to optimize the model parameters, and its description formula is:
  • step 4 comprises the following sub-steps:
  • Step 401 Pass all the samples of known categories in the database through the encoding module, and store the output high-dimensional encoded expression in the database;
  • Step 402 After the vibration signal of the target bearing passes through the preprocessing and encoding module, it is then simultaneously used as the input of the matching module with the high-dimensional encoding of the known category in the database, so as to obtain a matching score with each category in the known category;
  • Step 403 Take the maximum value among all matching scores of each category in the known categories, and its corresponding category is the failure mode of the target bearing.
  • step 403 the maximum value of the matching scores of all and each category in the known categories is taken, and the description formula is:
  • class is the class corresponding to the maximum matching score, that is, the failure mode of the target bearing.
  • the convolutional neural network adopts a deep neural network with sparse connection and parameter sharing characteristics.
  • the present invention also provides a system for the aforementioned method for diagnosing bearing failure modes oriented to small sample data sets, the system comprising:
  • the preprocessing module is used to convert the one-dimensional vibration signal of the bearing into a two-dimensional signal through continuous wavelet transformation, and perform image gray value conversion on it;
  • the task generation module is used to randomly sample from the preprocessed image data to construct a learning task of multiple small sample sets, wherein each small sample set includes a sampling set and a query set;
  • an encoding module configured to perform function mapping on the samples of the sampling set and the query set to obtain an encoded expression in a higher dimensional space
  • the matching module is used to match the coding of the query set samples with the coding of each category in the sampling set, so as to obtain the corresponding categories of the query set samples;
  • the diagnosis module is used for collecting the vibration signal of the target bearing, and diagnosing the bearing failure mode according to the vibration signal of the target bearing and the bearing failure diagnosis model.
  • the basic structures of the encoding module and the matching module are both convolutional neural networks, and the convolutional neural networks use deep neural networks with sparse connection and parameter sharing characteristics.
  • the present invention has the following advantages:
  • Both the coding module and the matching module in the present invention are based on the convolutional neural network and have two characteristics of sparse connection and parameter sharing, which are extremely suitable for deep feature expression mining of image data.
  • the preprocessing module in the present invention can process non-stationary and nonlinear signals through the continuous wavelet transform method, so as to obtain a more robust feature expression.
  • the task generation module in the present invention can construct learning tasks of multiple small sample sets through image data, so that the model can learn transferable knowledge between different tasks and help the model to quickly adapt to new tasks.
  • FIG. 1 is a partial bearing signal preprocessing image of the present invention.
  • FIG. 2 is a schematic diagram of the bearing failure mode diagnosis framework of the present invention.
  • FIG. 3 is a schematic diagram of the model structure of the present invention.
  • FIG. 4 is a flow chart of the method of the present invention.
  • FIG. 5 is a schematic diagram of the fault diagnosis system of the present invention.
  • the present invention provides a bearing failure mode diagnosis method oriented to small sample data sets, as shown in FIG. 4 , including the following steps:
  • the bearing vibration timing signal collected in this example has four different working conditions. Each working condition includes 10 bearing failure modes. In addition to normal, there are 9 fault types, including three different faults. Locations: inner ring fault, ball fault, and outer ring fault, with three different fault sizes for each fault location.
  • the bearing fault feature map is formed, part of which is shown in Figure 1, and finally stored in the database of the server.
  • Step 2) specifically includes:
  • Step 201 The vibration data collected by the acceleration sensor is a one-dimensional continuous time series signal, and the signal is preprocessed by a continuous wavelet transform algorithm to obtain a two-dimensional signal;
  • Step 202 The two-dimensional signal is converted into image gray value and stored in the database in the form of image gray value conversion.
  • the description formula of the two-dimensional signal in step 201 is:
  • CWT f (a, b) is a two-dimensional signal
  • f(t) is a one-dimensional vibration signal
  • ⁇ (t) is the complex conjugate of the wavelet function ⁇ (t)
  • a and b represent the scaling and translation factors, respectively;
  • step 202 the two-dimensional signal is converted into an image gray value, and its description formula is:
  • image(CWT f (a,b)) is the image data after the two-dimensional signal is converted into gray value
  • max( ⁇ ) is the maximum value function
  • min( ⁇ ) is the minimum value function
  • Task i is the learning task, is the sampling set, is the query set, m is the number of samples in the sampling set, n is the number of samples in the query set, k and j are both natural numbers, and each sample in the sampling set and the query set is composed of a pair of fault features and fault modes;
  • sampling samples and query samples of each task pass through the encoding module to obtain high-dimensional encoded representations. After the coding values of the samples of the same class are averaged, they are spliced with the query samples in the feature dimension to form matching pairs:
  • Pair(l,k) is the matching pair
  • Cat( ) is the splicing function in the feature dimension
  • M i,l is the average value of the coded value of the l-th sample sample in the ith task, is the parameter of the encoding module
  • Each matching pair passes through the matching module to obtain a matching score:
  • r l,k ⁇ [0,1] is the matching score between the kth query set and the lth class
  • g ⁇ ( ) is the function map of the matching module
  • ⁇ is the parameter of the matching module
  • Loss is the error between the matching score predicted by the model and the actual matching score
  • the category corresponding to the maximum matching score is the failure mode of the target bearing:
  • class is the class corresponding to the maximum matching score, that is, the failure mode of the target bearing.
  • FIG. 5 is a schematic structural diagram of a bearing failure mode diagnosis system oriented to a small sample data set according to the present invention.
  • the bearing failure mode diagnosis system 10 for small sample data sets includes: a preprocessing module 100 , a task generation module 200 , an encoding module 300 , a matching module 400 , and a diagnosis module 500 .
  • the preprocessing module 100 converts the one-dimensional vibration signal of the bearing into a two-dimensional signal through continuous wavelet transformation, and performs image gray value conversion on it.
  • the task generation module 200 randomly samples from the preprocessed image data, so as to construct a learning task of multiple small sample sets, wherein each small sample set includes a sampling set and a query set.
  • the encoding module 300 performs functional mapping on the samples of the sample set and the query set to obtain the encoded expression in a higher dimensional space.
  • the matching module 400 matches the codes of the query set samples with the codes of various categories in the sample set, so as to obtain the corresponding categories of the query set samples.
  • the diagnosis model 500 is used to collect the vibration signal of the target bearing, and diagnose the bearing failure mode according to the vibration signal of the bearing and the bearing failure diagnosis model.
  • the system 10 of the embodiment of the present invention combines deep learning and meta-learning algorithms to improve diagnostic accuracy in small sample data sets.
  • the preprocessing module 100 can process non-stationary and nonlinear signals through the continuous wavelet transform method to obtain a more robust feature expression.
  • the task generation module 200 can construct learning tasks of multiple small sample sets through image data, so that the model can learn transferable knowledge between different tasks and help the model quickly adapt to new tasks.
  • both the encoding module 300 and the matching module 400 are based on a convolutional neural network, with two characteristics of sparse connection and parameter sharing, which are extremely suitable for deep feature expression mining of image data.

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Abstract

Un procédé de diagnostic de mode de défaillance de palier utilisant de petits ensembles de données d'échantillon, et un système. Le procédé comprend les étapes suivantes consistant : 1) à collecter, au moyen d'un capteur d'accélération, des données de signal de vibration d'un palier fonctionnant dans différentes conditions de travail de différents dispositifs ; 2) à prétraiter les signaux, à convertir les signaux unidimensionnels originaux en signaux bidimensionnels au moyen d'un algorithme de transformée en ondelettes continue, et à former des données d'image ; 3) à construire un cadre de modèle de diagnostic de défaillance de palier sur la base d'un réseau neuronal convolutif et comprenant un module de codage et un module d'adaptation, et à échantillonner de manière aléatoire les données d'image pour construire une tâche d'apprentissage de multiples petits ensembles d'échantillons, de façon à entraîner le modèle ; et 4) à acquérir des signaux de vibration d'un palier cible, et à diagnostiquer le mode de défaillance de palier selon le procédé de prétraitement et le modèle de diagnostic de défaillance de palier. En combinant des algorithmes d'apprentissage profond et de méta-apprentissage, la précision de diagnostic peut être améliorée lorsque le volume de données est insuffisant.
PCT/CN2021/093451 2020-10-13 2021-05-12 Procédé de diagnostic de mode de défaillance de palier utilisant de petits ensembles de données d'échantillon, et système WO2022077901A1 (fr)

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