A rolling bearing fault diagnosis method based on local preservation autoencoder

Abstract

The present invention discloses a rolling bearing fault diagnosis method based on a locality-preserving autoencoder. The method comprises the following steps: 1. Slicing and whitening raw vibration signal samples of rolling bearings in various healthy states, collected at different motor shaft speeds, to construct a global training sample set; 2. Randomly selecting a certain proportion of samples from the global training sample set to form a local training sample set; 3. Constructing a weight graph using a complexity-invariant distance measurement method; 4. Training a locality-preserving autoencoder model using the global and local training sample sets and the weight graph, extracting feature vectors of the raw vibration signal samples using the trained model network parameters to form a classifier training data set; and 5. Using the classifier training data set to train a classifier for bearing fault diagnosis. By preserving the local structural information of the data in the original space within the reconstructed space, the present invention obtains information-rich and discriminative features, thereby effectively improving fault identification.

Description

Technical Field

[0001] The present invention relates to the field of mechanical fault diagnosis, in particular to a rolling bearing fault diagnosis method based on a local preservation autoencoder. Background Art

[0002] With the manufacturing industry's growing demand for modernization and automation, mechanical equipment is rapidly developing toward large-scale, high-speed, and high-precision systems. Promptly and quickly diagnosing faults in complex systems can effectively prevent unnecessary downtime, severe economic losses, and even casualties. Therefore, accurate fault diagnosis is crucial to the reliable operation of mechanical equipment.

[0003] Intelligent fault diagnosis can adaptively learn fault identification features from large amounts of raw monitoring data and accurately diagnose the health status of machinery. Feature extraction is a key component of intelligent fault diagnosis and directly impacts diagnostic effectiveness. Unlike traditional feature extraction methods, which rely too heavily on experience and prior knowledge and are limited in their ability to extract features for multi-task scenarios, unsupervised feature learning can directly learn features for fault diagnosis from diagnostic data. Autoencoders are one of the most widely used unsupervised feature learning methods, but they only learn approximate representations of identity functions and cannot fully exploit the structural information of the data. Summary of the Invention

[0004] In order to address the shortcomings of the above-mentioned prior art, the present invention proposes a rolling bearing fault diagnosis method based on a local preservation autoencoder, in order to utilize unsupervised feature learning methods to mine fault information hidden in the original data, thereby improving the accuracy of rolling bearing fault diagnosis.

[0005] In order to achieve the above-mentioned object, the present invention adopts the following technical solutions:

[0006] The rolling bearing fault diagnosis method based on the local preservation autoencoder of the present invention is characterized in that it includes the following steps:

[0007] Step 1: Slice and whiten the original vibration signal samples of rolling bearings in various health states collected at different shaft speeds to construct a global training sample set. ;

[0008] Obtaining a sample set of vibration signals from rolling bearings ,in, For the vibration signal samples, P represents the total number of sampling points, yes The corresponding health status; C represents the type of health status;

[0009] right The overlap rate is , the fragment length is After the fragmentation processing and then whitening processing, the t-th local sample fragment set is obtained , thus obtaining the global training sample set ,in, express The i-th local sample segment in M ​​represents the total number of local sample segments for each vibration signal sample. ;

[0010] Step 2: From the global training sample set Randomly select a certain proportion The local sample fragments constitute the local training sample set ,in, express The kth local sample fragment extracted in, represents the total number of local sample fragments extracted, and ;

[0011] Step 3: Based on local training sample set , using the complexity-invariant distance measurement method to construct the weighted graph ,in, is the edge set of the weighted graph;

[0012] Step 4: Construct a local preservation autoencoder, including encoder h and decoder d, and and After processing, we get Corresponding global reconstruction results and Corresponding local reconstruction results ;

[0013] Step 5: Use formula (4) to construct the loss function Loss of the local preservation autoencoder:

[0014] (4)

[0015] In formula (4), The similarity matrix and the identity matrix The combination matrix, is the local retention penalty parameter, T represents the transpose, represents the trace of the matrix;

[0016] Step 6: Taking the minimum loss function Loss as the goal, use the L-BFGS algorithm to optimize the parameters of the local preservation autoencoder to obtain the trained local preservation autoencoder;

[0017] Step 7: Use the trained local preservation autoencoder to extract the vibration signal sample set The feature vector of ;

[0018] Will Input the trained local preservation autoencoder model for processing to obtain the tth local feature representation set , and After average pooling, we get The corresponding eigenvector , thus obtaining the training data set of the classifier ,in, express Local feature representation;

[0019] Step 8: The training data set of the classifier The input is processed in the classifier, and the loss function of the classifier is constructed using formula (5) , thereby training the classifier and obtaining the optimal classifier after training;

[0020] (5)

[0021] In formula (5), Is an indicator function, when the internal condition is met, is 1, otherwise, is 0; is the weight decay factor, Indicates the cth parameter corresponding to the classifier;

[0022] Step 9: Give any new vibration signal sample Input the trained local preservation autoencoder model for processing, and get The corresponding eigenvector , and then the eigenvector Input into the trained optimal classifier, and then use formula (4) to get Belong to Probability of healthy state , thereby realizing the classification of bearing faults;

[0023] (6)

[0024] In formula (4), is the cth parameter of the optimal classifier.

[0025] The rolling bearing fault diagnosis method based on the local preservation autoencoder according to the present invention is also characterized in that step 3 includes the following steps:

[0026] Step 3.1: Use the distance measurement formula with constant complexity to obtain of Nearest neighbor sample set ;

[0027] Step 3.2: Use formula (1) to obtain the similarity matrix :

[0028] (1)

[0029] In formula (1), express Hedi Extracted local sample fragments The relationship between season Otherwise, let ;

[0030] Step 3.3, Each local sample fragment in is used as the vertex of the weight graph, and the similarity between the vertices, that is, the connection weight, is used as the edge of the weight graph to construct the weight graph .

[0031] Furthermore, the step 4 includes the following steps:

[0032] Step 4.1: Global training sample set Composing the global data matrix , and input into the encoder h for processing, so as to obtain its corresponding hidden feature representation using formula (2) ;

[0033] (2)

[0034] In formula (2), , are the weight matrix and bias of encoder h, respectively, Indicates that copy The operator of the second is the activation function;

[0035] Step 4.2, Decoder Using formula (3) to represent the hidden features Perform decoding operation and get Corresponding global reconstruction results ;

[0036] (3)

[0037] In formula (3), , are the weight matrix and bias of decoder d respectively;

[0038] Step 4.3: From the local training sample set Composing local data matrix , and after following the encoding and decoding operations of steps 4.1-4.2, we get Corresponding local reconstruction results .

[0039] The electronic device of the present invention includes a memory and a processor, and is characterized in that the memory is used to store a program that supports the processor to execute the rolling bearing fault diagnosis method, and the processor is configured to execute the program stored in the memory.

[0040] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium. The characteristic of the computer program is that the steps of the rolling bearing fault diagnosis method are executed when the computer program is run by a processor.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. Because bearings typically operate under complex conditions of variable speed and load, their monitoring signals exhibit characteristics such as high nonlinearity and non-stationarity, high noise, and weak fault signatures. This invention achieves accurate reconstruction of data from the original space to the reconstructed space while preserving local characteristics by jointly optimizing the locality-preserving penalty term of the cost function of a standard autoencoder. This effectively improves the learning ability of discriminative features in an unsupervised framework and enhances the ability to extract complex bearing signal features.

[0043] 2. Because bearing monitoring signals are typically time series data, the commonly used Euclidean distance metric is not suitable for measuring the similarity between two time series data segments. This paper uses a complexity-invariant distance metric instead of the traditional Euclidean distance metric, thereby accurately assessing the similarity between two time series data segments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of the rolling bearing fault diagnosis method of the present invention;

[0045] Figure 2 This is a flow chart of the fault feature extraction method of the present invention. DETAILED DESCRIPTION

[0046] In this embodiment, Figure 1 As shown, a rolling bearing fault diagnosis method based on a local preservation autoencoder includes the following steps:

[0047] Step 1: Slice and whiten the original vibration signal samples of rolling bearings in various health states collected at different shaft speeds to construct a global training sample set. ;

[0048] Obtaining a sample set of vibration signals from rolling bearings ,in, For the vibration signal samples, P represents the total number of sampling points, yes The corresponding health status; C represents the type of health status;

[0049] right The overlap rate is , the fragment length is After the fragmentation processing and then whitening processing, the t-th local sample fragment set is obtained , thus obtaining the global training sample set ,in, express The i-th local sample segment in M ​​represents the total number of local sample segments for each vibration signal sample. .

[0050] Step 2: From the global training sample set Randomly select a certain proportion The local sample fragments constitute the local training sample set ,in, express The kth local sample fragment extracted in, represents the total number of local sample fragments extracted, and .

[0051] Step 3: Based on local training sample set ,The weight graph is constructed using the complexity invariant distance measurement method to obtain the local geometric structure of the vibration signal samples;

[0052] Step 3.1: Use the distance measurement formula with constant complexity to obtain of Nearest neighbor sample set ; Each sample It is a time series signal (i.e., time series data). Due to the time-shift characteristics of time series data, Euclidean distance is not suitable for processing time series data. Complexity invariant distance has higher measurement accuracy in evaluating the complexity information of two time series data segments. Therefore, complexity invariant distance is used to measure the similarity of two time series data segments.

[0053] Step 3.2: Use formula (1) to obtain the similarity matrix :

[0054] (1)

[0055] In formula (1), express Hedi Extracted local sample fragments The relationship between season Otherwise, let Formula (1) is a standard constrained quadratic programming problem, which consists of N sub-optimization problems. In each sub-problem, the optimization parameter is a K-dimensional vector, and the constraints include 1 equality constraint and K inequality constraints.

[0056] Step 3.3, Each local sample fragment in the weight graph is used as the vertex of the weight graph, and the similarity between the vertices (i.e., the connection weight) is used as the edge of the weight graph to construct the weight graph ; is the set of vertices of the graph, is the edge set of the weighted graph.

[0057] Step 4: Construct a local preservation autoencoder, including the encoder and decoder , and and After processing, we get Corresponding global reconstruction results and Corresponding local reconstruction results ;

[0058] Step 4.1: Global training sample set Each sample in is stacked together in columns to form a column of size The global data matrix , and input the encoder In the process, we can use formula (2) to Encode and get its corresponding hidden feature representation ;

[0059] (2)

[0060] In formula (2), , are the weight matrix and bias of the encoder h, respectively, and the operator Indicates that copy The operator of the second It is an activation function; the commonly used Sigmoid activation function has the advantages of nonlinearity and being differentiable everywhere;

[0061] Step 4.2, Decoder Using formula (3) to represent the hidden features Perform decoding operation and get Corresponding global reconstruction results ;

[0062] (3)

[0063] In formula (3), , are the weight matrix and bias of decoder d respectively;

[0064] Step 4.3: From the local training sample set Each sample in is stacked together in columns to form a column of size The local data matrix , and after following the encoding and decoding operations of steps 4.1-4.2, we get Corresponding local reconstruction results ; and All sizes are The data matrix is ​​encoded and decoded using the same principles as in formula (2) and formula (3). and The purpose is to construct a local structure of the original training samples, and to mine the discriminative feature information contained in the local structure by constructing a loss function to train the network.

[0065] Step 5: Use formula (4) to construct the loss function Loss of the local preservation autoencoder:

[0066] (4)

[0067] In formula (4), is composed of the similarity matrix and the identity matrix The combination matrix, is the local retention penalty parameter; represents the trace of the matrix.

[0068] Step 6: With the minimum loss function Loss as the goal, use the L-BFGS algorithm to optimize the parameters of the local preservation autoencoder to obtain the trained local preservation autoencoder. L-BFGS is a commonly used quasi-Newton method with advantages such as high computational efficiency and adaptive learning rate selection. Each iteration only requires providing the gradient and loss function value of the previous step to complete the current iteration.

[0069] Step 7: Use the trained local preservation autoencoder to extract the vibration signal sample set The feature vector of , the specific steps are as follows Figure 2 As shown, Input the trained local preservation autoencoder model for processing to obtain the tth local feature representation set , and After average pooling, we get The corresponding eigenvector , thus obtaining the training data set of the classifier ,in, express local feature representation.

[0070] Step 8: The training data set of the classifier The input is processed in the classifier, and the loss function of the classifier is constructed using formula (5) , thereby training the classifier and obtaining the optimal classifier after training;

[0071] (5)

[0072] In formula (5), Is an indicator function, when the internal condition is met, is 1, otherwise, is 0; is the weight decay factor, represents the cth parameter corresponding to the classifier; T represents transposition; Formula (5) can also be optimized using the L-BFGS algorithm.

[0073] Step 9: Give any new vibration signal sample Input the trained local preservation autoencoder model for processing, and get The corresponding eigenvector , and then the eigenvector Input into the trained optimal classifier, and then use formula (4) to get Belong to Probability of healthy state , thereby realizing the classification of bearing faults;

[0074] (6)

[0075] In formula (4), is the cth parameter of the optimal classifier, There are C elements in Corresponding to labels respectively, each label corresponds to a bearing fault category, according to The label corresponding to the largest element in can determine the current input signal When the bearing is detected, the fault type of the bearing can be determined, thereby realizing the fault diagnosis of the bearing.

[0076] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0077] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

Claims

1. A rolling bearing fault diagnosis method based on local preservation autoencoder, characterized in that: The following steps are involved: Step 1: Slice and whiten the original vibration signal samples of rolling bearings in various health states collected at different shaft speeds to construct a global training sample set. ; Obtaining a sample set of vibration signals from rolling bearings ,in, For the vibration signal samples, P represents the total number of sampling points, yes The corresponding health status; C represents the type of health status; right The overlap rate is , the fragment length is After the fragmentation processing and then whitening processing, the t-th local sample fragment set is obtained , thus obtaining the global training sample set ,in, express The i-th local sample segment in M ​​represents the total number of local sample segments for each vibration signal sample. ; Step 2: From the global training sample set Randomly select a certain proportion The local sample fragments constitute the local training sample set ,in, express The kth local sample fragment extracted in, represents the total number of local sample fragments extracted, and ; Step 3: Based on local training sample set , using the complexity-invariant distance measurement method to construct the weighted graph ,in, is the edge set of the weighted graph; Step 4: Construct a local preservation autoencoder, including encoder h and decoder d, and and After processing, we get Corresponding global reconstruction results and Corresponding local reconstruction results ; Step 5: Use formula (4) to construct the loss function Loss of the local preservation autoencoder: (4) In formula (4), The similarity matrix and the identity matrix The combination matrix, is the local retention penalty parameter, T represents the transpose, represents the trace of the matrix; Step 6: Taking the minimum loss function Loss as the goal, use the L-BFGS algorithm to optimize the parameters of the local preservation autoencoder to obtain the trained local preservation autoencoder; Step 7: Use the trained local preservation autoencoder to extract the vibration signal sample set The feature vector of ; Will Input the trained local preservation autoencoder model for processing to obtain the tth local feature representation set , and After average pooling, we get The corresponding eigenvector , thus obtaining the training data set of the classifier ,in, express Local feature representation; Step 8: The training data set of the classifier The input is processed in the classifier, and the loss function of the classifier is constructed using formula (5) , thereby training the classifier and obtaining the optimal classifier after training; (5) In formula (5), Is an indicator function, when the internal condition is met, is 1, otherwise, is 0; is the weight decay factor, Indicates the cth parameter corresponding to the classifier; Step 9: Give any new vibration signal sample Input the trained local preservation autoencoder model for processing, and get The corresponding eigenvector , and then the eigenvector Input into the trained optimal classifier, and then use formula (4) to get Belong to Probability of healthy state , thereby realizing the classification of bearing faults; (6) In formula (4), is the cth parameter of the optimal classifier.

2. The rolling bearing fault diagnosis method based on local preservation autoencoder according to claim 1, characterized in that: The step 3 comprises the following steps: Step 3.1: Use the distance measurement formula with constant complexity to obtain of Nearest neighbor sample set ; Step 3.2: Use formula (1) to obtain the similarity matrix : (1) In formula (1), express Hedi Extracted local sample fragments The relationship between season Otherwise, let ; Step 3.3, Each local sample fragment in is used as the vertex of the weight graph, and the similarity between the vertices, that is, the connection weight, is used as the edge of the weight graph to construct the weight graph .

3. The rolling bearing fault diagnosis method based on local preservation autoencoder according to claim 2, characterized in that: The step 4 comprises the following steps: Step 4.1: Global training sample set Composing the global data matrix , and input into the encoder h for processing, so as to obtain its corresponding hidden feature representation using formula (2) ; (2) In formula (2), , are the weight matrix and bias of encoder h, respectively, Indicates that copy The operator of the second is the activation function; Step 4.2, Decoder Using formula (3) to represent the hidden features Perform decoding operation and get Corresponding global reconstruction results ; (3) In formula (3), , are the weight matrix and bias of decoder d respectively; Step 4.3: From the local training sample set Composing local data matrix , and after following the encoding and decoding operations of steps 4.1-4.2, we get Corresponding local reconstruction results .

4. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the rolling bearing fault diagnosis method according to any one of claims 1 to 3, and the processor is configured to execute the program stored in the memory.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the rolling bearing fault diagnosis method according to any one of claims 1 to 3 are executed.

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