Bearing unbalance fault diagnosis method and system cooperatively guided by double classifiers

Abstract

The invention relates to a bearing unbalance fault diagnosis method and system cooperatively guided by double classifiers, and the method comprises the steps: collecting bearing vibration signals in each health state under different working conditions, carrying out the fast Fourier transform, converting the signals into frequency domain signals, obtaining a data set, and carrying out the classification of the data set; dividing the data set into source domain data and target domain data under different working conditions, and marking the source domain data; constructing a fault diagnosis model, wherein the fault diagnosis model comprises a shared feature extractor, a balance classifier and a standard classifier; constructing a loss function based on the source domain data and the target domain data; training the fault diagnosis model by using a loss function to obtain a trained fault diagnosis model; and inputting a vibration signal of the bearing to be detected into the trained fault diagnosis model to obtain a fault diagnosis result. The problems that in the prior art, the performance of a diagnosis model is scarce by labels, data distribution is unbalanced, and the generalization ability of the model is improved can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of bearing fault diagnosis, and in particular to a bearing imbalance fault diagnosis method and system guided by a dual classifier collaboration. Background Art

[0002] Intelligent fault diagnosis is one of the important research directions to improve industrial reliability. As a key component widely used in industrial systems, the health status of bearings has a direct impact on the operating efficiency and safety of the entire system. Therefore, research on intelligent fault diagnosis technology for bearings is not only an important means to improve the overall reliability of industrial systems, but also a key link to achieve innovative breakthroughs in current technological development.

[0003] In the prior art, the application of deep learning-based fault diagnosis methods in actual industrial scenarios faces many challenges. Such methods are highly dependent on a large amount of labeled fault data for training, but in actual industrial environments, obtaining sufficient labeled data often requires a lot of manpower and material resources, and it is difficult to collect labeled data because it is unacceptable for the machine to run for a long time under fault conditions. In addition, the scarcity of labeled data limits the robustness and generalization ability of the model, making it difficult to meet the needs of high-precision diagnosis in diverse and dynamic industrial scenarios. In addition, in actual industrial scenarios, mechanical equipment is in normal operation most of the time, and faults only occur occasionally. At the same time, strict safety requirements will quickly stop the equipment from running after a fault occurs, further limiting the opportunity to collect fault progress data. The probabilities of different fault types vary significantly, so that the data collected by the monitoring system are mainly normal operation data, and fault data is extremely scarce, resulting in a significant long-tail distribution of the data set. This imbalance in data distribution seriously weakens the generalization ability of the intelligent fault diagnosis model, and ultimately leads to poor diagnostic performance of the model in actual applications. Therefore, how to solve the problems of label scarcity, imbalanced data distribution, and improvement of model generalization ability has become a key problem that needs to be broken through in current intelligent fault diagnosis technology. Summary of the invention

[0004] To this end, the technical problem to be solved by the present invention is to overcome the problems in the prior art that the performance of the diagnostic model is affected by the scarcity of labels, the imbalance of data distribution, and the improvement of the generalization ability of the model, and provide a bearing imbalance fault diagnosis method and system guided by dual classifiers, wherein the fault diagnosis method includes the following steps:

[0005] S1: Collect bearing vibration signals in various health states under different working conditions, perform fast Fourier transform, convert them into frequency domain signals, obtain a data set, divide the data set into source domain data and target domain data under different working conditions, and mark the source domain data;

[0006] S2: constructing a fault diagnosis model, the fault diagnosis model comprising a shared feature extractor, a balanced classifier and a standard classifier; and constructing a loss function based on the source domain data and the target domain data;

[0007] S3: training the fault diagnosis model using the loss function to obtain a trained fault diagnosis model;

[0008] S4: inputting the vibration signal of the bearing to be tested into the trained fault diagnosis model to obtain a fault diagnosis result;

[0009] Wherein, in step S2, the method of constructing the loss function includes:

[0010] S21: using the shared feature extractor to perform feature extraction on the source domain data and the target domain data respectively, to obtain source domain data features and target domain data features;

[0011] S22: inputting the source domain data features into the balanced classifier and the standard classifier respectively to obtain a source domain balanced classification loss and a source domain standard classification loss;

[0012] S23: inputting the target domain data features into a standard classifier to generate a target domain data pseudo label for a balanced classifier; combining the original output of the standard classifier of the target domain data features with the output of the standard classifier of the target domain data features after post-logit adjustment to obtain an adjusted perception weight;

[0013] S24: obtaining a target domain classification loss of a balanced classifier by using the adjusted perception weight and the target domain pseudo-label; and obtaining a target domain classification loss of a standard classifier by filtering an original output of a standard classifier for target domain data features by using a confidence threshold;

[0014] S25: performing weighted calculation on the Coral loss, the source domain balanced classification loss, the source domain standard classification loss, the target domain classification loss of the balanced classifier, and the target domain classification loss of the standard classifier to obtain the loss function L total , which is expressed as follows:

[0015]

[0016] Among them, L cor Indicates Coral loss, represents the standard classification loss in the source domain, represents the source domain balanced classification loss, represents the target domain classification loss of the standard classifier, represents the target domain classification loss of the balanced classifier, and μ is a hyperparameter.

[0017] In one embodiment of the present invention, step S21 further includes: using the Coral loss to align the feature distribution of the source domain data features and the target domain data features.

[0018] In one embodiment of the present invention, the Coral loss L cor It is expressed as follows:

[0019]

[0020] Among them, d E represents the output dimension of the shared feature extractor, x s and x t represent the source domain data and the target domain data respectively, ||·|| represents the Frobenius norm, C s and C t Respectively represent the covariance matrix of the source domain data and the target domain data, n s and n t Respectively represent the source domain data features and the target domain data features, and 1 is a column vector whose elements are equal to 1.

[0021] In one embodiment of the present invention, the source domain balanced classification loss It is expressed as follows:

[0022]

[0023] Among them, n s is the total number of samples of the source domain data, x s,i is the i-th sample in the source domain data, y s,i is the label of the i-th sample in the source domain data, represents the output of the balanced classifier of the i-th sample in the source domain data, represents the logit adjustment factor of the balanced classifier, π s,[k] =n s,[k] / n s represents the observation frequency of the kth category in the source domain data, n s,[k] is the total number of samples in the kth class in the source domain data, represents the temperature coefficient of the balanced classifier.

[0024] In one embodiment of the present invention, the source domain standard classification loss It is expressed as follows:

[0025]

[0026] in, represents the output of the balanced classifier of the i-th sample in the source domain data, represents the logit adjustment factor of the standard classifier, represents the temperature coefficient of the standard classifier, represents the observation frequency of the kth class in the target domain data estimated by the balanced classifier, λ is a trade-off parameter, represents the class distribution term of the kth class in the target domain data in the first b-1 batches estimated by the balanced classifier, Represents the class distribution item of the kth class in the target domain data in the current bth batch.

[0027] In one embodiment of the present invention, the class distribution item of the kth class in the target domain data in the current bth batch is The calculation formula is:

[0028]

[0029] Among them, n b is the number of samples in a batch, and δ(·) represents the Softmax function.

[0030] In one embodiment of the present invention, in step S23, a pseudo label of the target domain data for the balanced classifier is generated. The calculation formula is expressed as:

[0031]

[0032] in, represents the target class distribution term estimated by the standard classifier, is the temperature coefficient;

[0033] Get the adjusted perception weight w(x t,i ) is calculated as:

[0034] w(x t,i )=α m β(x t,i )

[0035] Among them, x t,i is the i-th sample in the target domain data, β(x t,i ) represents the logit consistency factor, max(·) represents the maximum value operation, δ(·) represents the Softmax function; α m represents the adjustment of the perception factor in the mth training step, n b is the number of samples in a batch.

[0036] In one embodiment of the present invention, the target domain classification loss of the standard classifier is It is expressed as follows:

[0037]

[0038] Among them, 1{·} is an indicator function, which returns 1 if the condition is met, otherwise it returns 0, T represents the confidence threshold, is the pseudo label of the target domain data of the standard classifier, is the pseudo label of the i-th sample in the k-th class in the target domain data of the standard classifier, x t,i is the i-th sample in the target domain data, x t,i,[k] is the i-th sample in the k-th class in the target domain data, n t is the total number of samples of the target domain data, and K represents the total number of categories of the target domain data.

[0039] In one embodiment of the present invention, the target domain classification loss of the balanced classifier is It is expressed as follows:

[0040]

[0041] Among them, w(x t,i ) is used to adjust the perception weight, n t is the total number of samples of the target domain data, is the pseudo label of the i-th sample in the target domain data for the balanced classifier, is the pseudo label of the i-th sample in the k-th class in the target domain data for the balanced classifier, x t,i is the i-th sample in the target domain data, x t,i,[k] is the i-th sample in the k-th class in the target domain data, n t is the total number of samples of the target domain data, and K represents the total number of categories of the target domain data.

[0042] Based on the same inventive concept, the present invention also provides a bearing imbalance fault diagnosis system guided by dual classifiers in collaboration, which is used to implement the steps of the bearing imbalance fault diagnosis method guided by dual classifiers in collaboration, and the bearing imbalance fault diagnosis system guided by dual classifiers in collaboration includes the following modules:

[0043] A data set construction module is used to collect bearing vibration signals in various health states under different working conditions, perform fast Fourier transform, convert them into frequency domain signals, obtain a data set, divide the data set into source domain data and target domain data under different working conditions, and mark the source domain data;

[0044] A model building and loss function design module, used to build a fault diagnosis model, the fault diagnosis model includes a shared feature extractor, a balanced classifier and a standard classifier; and to build a loss function based on the source domain data and the target domain data;

[0045] A model training module, used to train the fault diagnosis model using the loss function to obtain a trained fault diagnosis model;

[0046] A fault diagnosis module, used for inputting the vibration signal of the bearing to be tested into the trained fault diagnosis model to obtain a fault diagnosis result;

[0047] The model construction and loss function design module includes a loss function construction submodule, and the loss function construction submodule is configured as follows:

[0048] Using the shared feature extractor to extract features from the source domain data and the target domain data respectively, to obtain source domain data features and target domain data features;

[0049] Inputting the source domain data features into the balanced classifier and the standard classifier respectively to obtain a source domain balanced classification loss and a source domain standard classification loss;

[0050] Inputting the target domain data features into a standard classifier to generate a target domain data pseudo label for a balanced classifier; combining the original output of the standard classifier of the target domain data features with the output of the standard classifier of the target domain data features after post-logit adjustment to obtain an adjusted perception weight;

[0051] Using the adjusted perception weights and the target domain pseudo labels, a target domain classification loss of a balanced classifier is obtained; and by using a confidence threshold, an original output of a standard classifier for target domain data features is screened to obtain a target domain classification loss of a standard classifier;

[0052] The loss function is obtained by performing weighted calculation on the Coral loss, the source domain balanced classification loss, the source domain standard classification loss, the target domain classification loss of the balanced classifier, and the target domain classification loss of the standard classifier.

[0053] The above technical solution of the present invention has the following advantages compared with the prior art:

[0054] The present invention designs a dual classifier so that the model can almost evenly guarantee the classification performance of each category. Under the synergistic effect of the dual classifier, the standard classifier can better adapt to the category distribution of the target domain. At the same time, through the carefully designed mechanism of adjusting the perception weight, the negative impact of unreliable pseudo-labels is effectively suppressed, thereby further improving the generalization ability of the model in the target domain of unknown category distribution and significantly improving the accuracy of imbalanced fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0056] Figure 1 It is a flow chart of a bearing imbalance fault diagnosis method guided by dual classifier collaboration provided in the first embodiment of the present invention;

[0057] Figure 2 is a flow chart of the loss function construction method provided in the first embodiment of the present invention;

[0058] Figure 3 This is a specific implementation flow chart of a bearing imbalance fault diagnosis method guided by dual classifier collaboration provided in the first embodiment of the present invention;

[0059] Figure 4 It is a test diagram of the bearing data generation test bench of the present invention;

[0060] Figure 5 It is a feature visualization diagram of the present invention under five different target domain class distribution conditions;

[0061] Figure 6 It is a structural schematic diagram of a bearing imbalance fault diagnosis system guided by dual classifier collaboration provided in the second embodiment of the present invention;

[0062] Explanation of the reference numerals in the specification: 100, data set construction module; 200, model construction and loss function design module; 210, loss function construction sub-module; 300, model training module; 400, fault diagnosis module. DETAILED DESCRIPTION

[0063] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0064] Embodiment 1

[0065] See also Figures 1 to 3As shown, the present invention provides a bearing imbalance fault diagnosis method guided by dual classifier collaboration, and the fault diagnosis method comprises the following steps:

[0066] S1: Collect bearing vibration signals in various health states under different working conditions, perform fast Fourier transform, convert them into frequency domain signals, obtain a data set, divide the data set into source domain data and target domain data under different working conditions, and mark the source domain data;

[0067] S2: constructing a fault diagnosis model, the fault diagnosis model comprising a shared feature extractor, a balanced classifier and a standard classifier; and constructing a loss function based on the source domain data and the target domain data;

[0068] S3: training the fault diagnosis model using the loss function to obtain a trained fault diagnosis model;

[0069] S4: inputting the vibration signal of the bearing to be tested into the trained fault diagnosis model to obtain a fault diagnosis result;

[0070] Among them, in step S2, the method of constructing the loss function is as follows Figure 2 As shown, including:

[0071] S21: using the shared feature extractor to perform feature extraction on the source domain data and the target domain data respectively, to obtain source domain data features and target domain data features;

[0072] S22: inputting the source domain data features into the balanced classifier and the standard classifier respectively to obtain a source domain balanced classification loss and a source domain standard classification loss;

[0073] S23: inputting the target domain data features into a standard classifier to generate a target domain data pseudo label for a balanced classifier; combining the original output of the standard classifier of the target domain data features with the output of the standard classifier of the target domain data features after post-logit adjustment to obtain an adjusted perception weight;

[0074] S24: obtaining a target domain classification loss of a balanced classifier by using the adjusted perception weight and the target domain pseudo-label; and obtaining a target domain classification loss of a standard classifier by filtering an original output of a standard classifier for target domain data features by using a confidence threshold;

[0075] S25: performing weighted calculation on the Coral loss, the source domain balanced classification loss, the source domain standard classification loss, the target domain classification loss of the balanced classifier, and the target domain classification loss of the standard classifier to obtain the loss function L total , which is expressed as follows:

[0076]

[0077] Among them, L cor Indicates Coral loss, represents the standard classification loss in the source domain, represents the source domain balanced classification loss, represents the target domain classification loss of the standard classifier, represents the target domain classification loss of the balanced classifier, and μ is a hyperparameter.

[0078] It can be seen from the above technical solutions that the present invention can solve the following problems existing in the prior art:

[0079] (1) Solve the problem of label scarcity: In the training process, not only the labeled source domain data but also the unlabeled target domain data are used. The features of the target domain data are extracted by a shared feature extractor, and pseudo labels are generated by a standard classifier. These pseudo labels supplement the label information to a certain extent. In order to reduce the noise that may be caused by pseudo labels, the present invention designs a method for adjusting perceptual weights. This method combines the original output of the standard classifier and the output after post-logit adjustment to more accurately evaluate the reliability of the pseudo labels, thereby giving different weights during the training process and reducing the negative impact of unreliable pseudo labels.

[0080] (2) Solve the problem of unbalanced data distribution: Two parallel classifier modules, a balanced classifier and a standard classifier, are constructed. Each of them has different initialization parameters or structures, so that more diverse feature information can be captured during training. When constructing the loss function, the present invention comprehensively considers the source domain balanced classification loss, the source domain standard classification loss, the target domain classification loss of the balanced classifier, and the target domain classification loss of the standard classifier, and by weighted summing these losses, the model pays more attention to the minority categories during training, thereby reducing the impact of unbalanced data distribution.

[0081] (3) Improve the generalization ability of the model: A feature sharing strategy is adopted. By sharing the feature extractor, the balanced classifier and the standard classifier can share some feature information, which is conducive to the model learning more general feature representations from large-scale data, thereby enhancing the generalization ability of the model. At the same time, the Coral loss is introduced into the loss function. This loss term is used to quantify the difference between the data distribution of the source domain and the target domain. By minimizing the Coral loss, the model can better adapt to the data distribution of the target domain, thereby improving the generalization performance of the model in unknown environments.

[0082] Furthermore, step S21 further includes: using the Coral loss to align the feature distribution of the source domain data features and the target domain data features. cor It is expressed as follows:

[0083]

[0084]

[0085] Among them, d E represents the output dimension of the shared feature extractor, x s and x t represent the source domain data and the target domain data respectively, ||·|| represents the Frobenius norm, C s and C t Respectively represent the covariance matrix of the source domain data and the target domain data, n s and n t Respectively represent the source domain data features and the target domain data features, and 1 is a column vector whose elements are equal to 1.

[0086] Furthermore, the source domain balanced classification loss It is expressed as follows:

[0087]

[0088] Among them, n s is the total number of samples of the source domain data, x s,i is the i-th sample in the source domain data, y s,i is the label of the i-th sample in the source domain data, represents the output of the balanced classifier of the i-th sample in the source domain data, represents the logit adjustment factor of the balanced classifier, π s,[k] =n s,[k] / n s represents the observation frequency of the kth category in the source domain data, n s,[k] is the total number of samples in the kth class in the source domain data, represents the temperature coefficient of the balanced classifier.

[0089] Furthermore, the source domain standard classification loss It is expressed as follows:

[0090]

[0091] in, represents the output of the balanced classifier of the i-th sample in the source domain data, represents the logit adjustment factor of the standard classifier, represents the temperature coefficient of the standard classifier, represents the observation frequency of the kth class in the target domain data estimated by the balanced classifier, λ is a trade-off parameter, represents the class distribution term of the kth class in the target domain data in the first b-1 batches estimated by the balanced classifier, Represents the class distribution item of the kth class in the target domain data in the current bth batch.

[0092] Furthermore, the class distribution item of the kth class in the target domain data in the current bth batch is The calculation formula is:

[0093]

[0094] Among them, n b is the number of samples in a batch, and δ(·) represents the Softmax function.

[0095] Further, in step S23, a pseudo label of the target domain data for the balanced classifier is generated. The calculation formula is expressed as:

[0096]

[0097] in, represents the target class distribution term estimated by the standard classifier, is the temperature coefficient;

[0098] Get the adjusted perception weight w(x t,i ) is calculated as:

[0099] w(x t,i )=α m β(x t,i )

[0100] Among them, x t,i is the i-th sample in the target domain data, β(x t,i ) represents the logit consistency factor, max(·) represents the maximum value operation, δ(·) represents the Softmax function; α m represents the adjustment of the perception factor in the mth training step, n b is the number of samples in a batch.

[0101] Furthermore, the target domain classification loss of the standard classifier is It is expressed as follows:

[0102]

[0103] Among them, 1{·} is an indicator function, which returns 1 if the condition is met, otherwise it returns 0, T represents the confidence threshold, is the pseudo label of the target domain data of the standard classifier, is the pseudo label of the i-th sample in the k-th class in the target domain data of the standard classifier, x t,i is the i-th sample in the target domain data, x t,i,[k] is the i-th sample in the k-th class in the target domain data, n t is the total number of samples of the target domain data, and K represents the total number of categories of the target domain data.

[0104] Furthermore, the target domain classification loss of the balanced classifier is It is expressed as follows:

[0105]

[0106] Among them, w(x t,i ) is used to adjust the perception weight, n t is the total number of samples of the target domain data, is the pseudo label of the i-th sample in the target domain data for the balanced classifier, is the pseudo label of the i-th sample in the k-th class in the target domain data for the balanced classifier, x t,i is the i-th sample in the target domain data, x t,i,[k] is the i-th sample in the k-th class in the target domain data, n t is the total number of samples of the target domain data, and K represents the total number of categories of the target domain data.

[0107] Based on the above embodiments, this embodiment uses Figure 4 The bearing test bench shown collects vibration signals and tests the bearing type. Using wire cutting technology, three faults are created, namely inner race fault (IF), outer race fault (OF), and ball fault (BF). In addition, each fault contains two fault size levels of 0.2mm and 0.4mm. After adding the normal condition (NC), the bearing dataset has 7 health conditions. Table 1 shows the fault labels of the bearing dataset:

[0108] Table 1 Description of fault labels in the bearing dataset

[0109]

[0110] The vibration signal of the test bearing is measured by a sensor located on the top of the bearing fixing bracket with a sampling frequency of 10kHz. There are three sub-datasets in the bearing dataset, namely L1 (0kN), L2 (1kN) and L3 (2kN). The source domain data follows a long-tail class distribution, while the target domain data follows an unknown class distribution. In order to facilitate the construction of the diagnosis task, 5 typical class distributions are designed for the target domain.

[0111] For five typical class distributions, the number of samples is set for each class in the target domain, as shown in Table 2. Except for the uniform distribution, the other four class distributions have two different sample number settings. In addition, two sample number settings are designed for the long-tail source domain. After setting the sample numbers of the source and target domains, 18 diagnostic tasks are designed for the bearing dataset, as shown in Table 3. For example, in Task A1, the SCU dataset across three working conditions (L0, L1, and L2) is treated as a separate domain and used as the source or target domain. When one condition (such as L0) is used as the source domain and the other condition (such as L1) is used as the target domain, a sub-transfer task (L0→L1) is created. This setting results in 6 sub-transfer tasks in A1 (L0→L1, L0→L2, L1→L0, L1→L2, L2→L0, and L2→L1). Therefore, the evaluation metrics of Task A1 are calculated by averaging these six sub-transfer tasks. Following this structure, the configurations of other diagnostic tasks are similar.

[0112] Table 2 Sample number settings for each class under different class distributions of source domain and target domain

[0113]

[0114] Table 3 Diagnostic task description

[0115]

[0116] The detailed structural parameter settings of the shared feature extractor, standard classifier and balanced classifier are shown in Table 4:

[0117] Table 4 Description of structured parameters

[0118]

[0119] In Table 4, “Convolution” represents the convolution layer, and “Max_Pooling” represents the maximum pooling layer. The “Adaptive_Pooling” layer adjusts the pooling size according to the input dimension. “Kernel_size=15*16” specifies that the kernel width is 15 with 16 output channels, and “padding=invalid” indicates that no padding operation is applied in the convolution layer. “ReLU” represents the rectified linear unit activation function, and “FC” represents the fully connected layer. The main parameter settings for the model training and fine-tuning stages are shown in Table 5. In addition, the stochastic gradient descent (SGD) algorithm is selected for model training.

[0120] Table 5 Main parameter settings of the present invention

[0121]

[0122] In order to verify the superiority of the method of the present invention, five methods were selected for comparison, including two homemade methods and two latest methods, as described below:

[0123] (1) Focal: Focal loss is a mature method to solve class imbalance by increasing the loss weight of the minority class, thereby shifting the focus of the model to more difficult samples.

[0124] (2) ACIL: Adaptive class imbalance loss adaptively emphasizes difficult-to-classify classes through class-level weighting terms, enhances the inter-class margin through boundary regularization, and improves the detection sensitivity of the model for minority classes.

[0125] (3) SMOTE: Synthetic Minority Oversampling Technique SMOTE solves the class imbalance problem by generating synthetic samples for the minority class. It expands the dataset by inserting between existing samples of the minority class.

[0126] (4) ACWGAN-GP: ACWGAN-GP addresses the data imbalance problem in fault diagnosis by generating high-quality samples for the minority class and improving the diagnosis accuracy and dataset balance through progressive enhancement with real synthetic samples.

[0127] (5) CA-SupCon: Class-Aware Supervised Contrastive Learning The CA-SupCon method enhances imbalanced fault diagnosis by combining supervised contrastive learning with a class-aware sampler, obtaining a discriminative feature space with better intra-class compactness and inter-class separability for minority class faults.

[0128] Table 6 shows the comparison of detection accuracy of 18 diagnostic tasks:

[0129] Table 6 Comparison of test accuracy between this method and the comparative method

[0130]

[0131] Table 6 summarizes the diagnostic accuracy of the proposed DCS and the five compared methods. As can be seen from Table 6, Focal has the lowest performance, with an average accuracy of 60.9% and 61.3% on the two datasets, respectively. This shows that the focal strategy of assigning greater weight to the minority class in the source domain fails to address the challenges brought by different target class distributions. Compared with Focal, ACIL significantly improves the accuracy by optimizing the margins of the classification boundary. However, it still lacks customized strategies for different target class distributions, resulting in performance that is still lower than the proposed DCS. SMOTE generates samples for the minority class through linear interpolation, which assumes that linear data distributions are rarely aligned with real-world scenarios, resulting in poor sample quality and limited adaptability to changes in target class distribution. ACWGAN-GP significantly improves the quality of generated samples by enhancing GAN. Although the source domain can be transformed into an approximately uniform distribution through data augmentation, its accuracy drops significantly when dealing with non-uniformly distributed target domains.

[0132] As shown in Table 6, the accuracy of ACWGAN-GP on tasks A5 and A6 is significantly higher than that of other tasks. Compared with CA-SupCon, the average accuracy of the proposed DCS on the two datasets is improved by 10.2% and 9.4%, respectively, and it is better than CA-SupCon in all diagnosis tasks. This shows that the proposed DCS can better handle different target class distributions and more effectively solve the problem of class distribution deviation.

[0133] Figure 5 The feature visualization results are shown in five different target domain category distribution scenarios. These five distributions are A4 (long-tail distribution), A6 (uniform distribution), A10 (reverse long-tail distribution), A14 (head-heavy and tail-light distribution), and A18 (intermediate distribution). Figure 5 It can be found that in the A4, A6, and A18 tasks where the target domain presents long-tail distribution, uniform distribution, and intermediate distribution, the clustering effect of the method of the present invention on the samples of the corresponding categories in the source domain and the target domain is particularly significant, and the classification boundaries between the categories are very clear. However, in the A10 task with inverse long-tail distribution, the classification performance of the two minority categories (6 and 7) in the source domain is relatively poor, and there is a certain confusion between these two categories of samples and the second category samples. This phenomenon shows that when there is a large difference in the category distribution between the source domain and the target domain, the insufficient number of samples of the minority category in the source domain will have a negative impact on the classification effect of the corresponding category in the target domain. In general, the method of the present invention can obtain satisfactory clustering effects under various category distribution conditions, which fully verifies the effectiveness and applicability of the method of the present invention.

[0134] Embodiment 2

[0135] Based on the same inventive concept as that of the first embodiment, the present invention further provides a bearing imbalance fault diagnosis system guided by a dual classifier collaboration, which is used to implement the steps of the bearing imbalance fault diagnosis method guided by a dual classifier collaboration described in the first embodiment. Figure 6 As shown, the bearing imbalance fault diagnosis system guided by dual classifier collaboration includes the following modules:

[0136] The data set construction module 100 is used to collect bearing vibration signals in various health states under different working conditions, and perform fast Fourier transform to convert the signals into frequency domain signals to obtain a data set, divide the data set into source domain data and target domain data under different working conditions, and mark the source domain data;

[0137] A model building and loss function design module 200 is used to build a fault diagnosis model, the fault diagnosis model includes a shared feature extractor, a balanced classifier and a standard classifier; and to build a loss function based on the source domain data and the target domain data;

[0138] A model training module 300, used to train the fault diagnosis model using the loss function to obtain a trained fault diagnosis model;

[0139] A fault diagnosis module 400 is used to input the vibration signal of the bearing to be tested into the trained fault diagnosis model to obtain a fault diagnosis result;

[0140] The model construction and loss function design module 200 includes a loss function construction submodule 210, and the loss function construction submodule 210 is configured as follows:

[0141] Using the shared feature extractor to extract features from the source domain data and the target domain data respectively, to obtain source domain data features and target domain data features;

[0142] Inputting the source domain data features into the balanced classifier and the standard classifier respectively to obtain a source domain balanced classification loss and a source domain standard classification loss;

[0143] Inputting the target domain data features into a standard classifier to generate a target domain data pseudo label for a balanced classifier; combining the original output of the standard classifier of the target domain data features with the output of the standard classifier of the target domain data features after post-logit adjustment to obtain an adjusted perception weight;

[0144] Using the adjusted perception weights and the target domain pseudo labels, a target domain classification loss of a balanced classifier is obtained; and by using a confidence threshold, an original output of a standard classifier for target domain data features is screened to obtain a target domain classification loss of a standard classifier;

[0145] The loss function is obtained by performing weighted calculation on the Coral loss, the source domain balanced classification loss, the source domain standard classification loss, the target domain classification loss of the balanced classifier, and the target domain classification loss of the standard classifier.

[0146] A bearing imbalance fault diagnosis system guided by dual classifiers in collaboration proposed in this embodiment is used to implement the aforementioned bearing imbalance fault diagnosis method guided by dual classifiers in collaboration. Therefore, the specific implementation method in the synchronous response single-point displacement monitoring system can be seen from the embodiment part of the aforementioned bearing imbalance fault diagnosis method guided by dual classifiers in collaboration. For example, the data set construction module 100, the model construction and loss function design module 200, the model training module 300 and the fault diagnosis module 400 are respectively used to implement steps S1 to S4 in the bearing imbalance fault diagnosis method guided by dual classifiers in embodiment one. Accordingly, the functions implemented by the loss function construction submodule correspond to steps S21 to S25 in the bearing imbalance fault diagnosis method guided by dual classifiers in embodiment one. Therefore, its specific implementation method can refer to the description of the corresponding various partial embodiments. In order to avoid redundancy, it will not be repeated here.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A bearing imbalance fault diagnosis method guided by dual classifier collaboration, characterized in that: The following steps are involved: S1: Collect bearing vibration signals in various health states under different working conditions, perform fast Fourier transform, convert them into frequency domain signals, obtain a data set, divide the data set into source domain data and target domain data under different working conditions, and mark the source domain data; S2: constructing a fault diagnosis model, the fault diagnosis model comprising a shared feature extractor, a balanced classifier and a standard classifier; and constructing a loss function based on the source domain data and the target domain data; S3: training the fault diagnosis model using the loss function to obtain a trained fault diagnosis model; S4: inputting the vibration signal of the bearing to be tested into the trained fault diagnosis model to obtain a fault diagnosis result; Wherein, in step S2, the method of constructing the loss function includes: S21: using the shared feature extractor to perform feature extraction on the source domain data and the target domain data respectively, to obtain source domain data features and target domain data features; S22: inputting the source domain data features into the balanced classifier and the standard classifier respectively to obtain a source domain balanced classification loss and a source domain standard classification loss; S23: inputting the target domain data features into a standard classifier to generate a target domain data pseudo label for a balanced classifier; combining the original output of the standard classifier of the target domain data features with the output of the standard classifier of the target domain data features after post-logit adjustment to obtain an adjusted perception weight; S24: obtaining a target domain classification loss of a balanced classifier by using the adjusted perception weight and the target domain pseudo-label; and obtaining a target domain classification loss of a standard classifier by filtering an original output of a standard classifier for target domain data features by using a confidence threshold; S25: performing weighted calculation on the Coral loss, the source domain balanced classification loss, the source domain standard classification loss, the target domain classification loss of the balanced classifier, and the target domain classification loss of the standard classifier to obtain the loss function L total , which is expressed as follows: Among them, L cor Indicates Coral loss, represents the standard classification loss in the source domain, represents the source domain balanced classification loss, represents the target domain classification loss of the standard classifier, represents the target domain classification loss of the balanced classifier, and μ is a hyperparameter.

2. The bearing imbalance fault diagnosis method guided by dual classifier collaboration according to claim 1 is characterized in that: Step S21 also includes: using the Coral loss to align the feature distribution of the source domain data features and the target domain data features.

3. The bearing imbalance fault diagnosis method guided by dual classifier collaboration according to claim 1 or 2, characterized in that: The Coral loss L cor It is expressed as follows: Among them, d E represents the output dimension of the shared feature extractor, x s and x t represent the source domain data and the target domain data respectively, ||·|| represents the Frobenius norm, C s and C t Respectively represent the covariance matrix of the source domain data and the target domain data, n s and n t Respectively represent the source domain data features and the target domain data features, and 1 is a column vector whose elements are equal to 1.

4. The bearing imbalance fault diagnosis method guided by dual classifier collaboration according to claim 1 is characterized in that: The source domain balanced classification loss It is expressed as follows: Among them, n s is the total number of samples of the source domain data, x s,i is the i-th sample in the source domain data, y s,i is the label of the i-th sample in the source domain data, represents the output of the balanced classifier of the i-th sample in the source domain data, represents the logit adjustment factor of the balanced classifier, π s,[k] =n s,[k] / n s represents the observation frequency of the kth category in the source domain data, n s,[k] is the total number of samples in the kth class in the source domain data, represents the temperature coefficient of the balanced classifier.

5. The bearing imbalance fault diagnosis method guided by dual classifier collaboration according to claim 1 is characterized in that: The source domain standard classification loss It is expressed as follows: in, represents the output of the balanced classifier of the i-th sample in the source domain data, represents the logit adjustment factor of the standard classifier, represents the temperature coefficient of the standard classifier, represents the observation frequency of the kth class in the target domain data estimated by the balanced classifier, λ is a trade-off parameter, represents the class distribution term of the kth class in the target domain data in the first b-1 batches estimated by the balanced classifier, Represents the class distribution item of the kth class in the target domain data in the current bth batch.

6. The bearing imbalance fault diagnosis method guided by dual classifier collaboration according to claim 5 is characterized in that: The class distribution item of the kth class in the target domain data in the current bth batch The calculation formula is: Among them, n b is the number of samples in a batch, and δ(·) represents the Softmax function.

7. The bearing imbalance fault diagnosis method guided by dual classifier collaboration according to claim 1 is characterized in that: In step S23, a pseudo label of the target domain data for the balanced classifier is generated The calculation formula is expressed as: in, represents the target class distribution term estimated by the standard classifier, is the temperature coefficient; Get the adjusted perception weight w(x t,i ) is calculated as: w(x t,i )=a m ·β(x t,i ) Among them, x t,i is the i-th sample in the target domain data, β(x t,i ) represents the logit consistency factor, max(·) represents the maximum value operation, δ(·) represents the Softmax function; α m represents the adjustment of the perception factor in the mth training step, n b is the number of samples in a batch.

8. The bearing imbalance fault diagnosis method guided by dual classifier collaboration according to claim 1, characterized in that: The target domain classification loss of the standard classifier It is expressed as follows: Among them, 1{·} is an indicator function, which returns 1 if the condition is met, otherwise it returns 0, T represents the confidence threshold, is the pseudo label of the target domain data of the standard classifier, is the pseudo label of the i-th sample in the k-th class in the target domain data of the standard classifier, x t,i is the i-th sample in the target domain data, x t,i,[k] is the i-th sample in the k-th class in the target domain data, n t is the total number of samples of the target domain data, and K represents the total number of categories of the target domain data.

9. The bearing imbalance fault diagnosis method guided by dual classifier collaboration according to claim 1, characterized in that: The target domain classification loss of the balanced classifier It is expressed as follows: Among them, w(x t,i ) is used to adjust the perception weight, n t is the total number of samples of the target domain data, is the pseudo label of the i-th sample in the target domain data for the balanced classifier, is the pseudo label of the i-th sample in the k-th class in the target domain data for the balanced classifier, x t,i is the i-th sample in the target domain data, x t,i,[k] is the i-th sample in the k-th class in the target domain data, n t is the total number of samples of the target domain data, and K represents the total number of categories of the target domain data.

10. A bearing imbalance fault diagnosis system guided by dual classifier collaboration, characterized in that: The method for diagnosing a bearing imbalance fault using a dual classifier cooperative guidance method according to any one of claims 1 to 9 is used to implement the steps of the method, wherein the dual classifier cooperative guidance bearing imbalance fault diagnosis system comprises the following modules: A data set construction module is used to collect bearing vibration signals in various health states under different working conditions, and perform fast Fourier transform to convert them into frequency domain signals to obtain a data set, divide the data set into source domain data and target domain data under different working conditions, and mark the source domain data; A model building and loss function design module, used to build a fault diagnosis model, the fault diagnosis model includes a shared feature extractor, a balanced classifier and a standard classifier; and to build a loss function based on the source domain data and the target domain data; A model training module, used to train the fault diagnosis model using the loss function to obtain a trained fault diagnosis model; A fault diagnosis module, used for inputting the vibration signal of the bearing to be tested into the trained fault diagnosis model to obtain a fault diagnosis result; The model construction and loss function design module includes a loss function construction submodule, and the loss function construction submodule is configured as follows: Using the shared feature extractor to extract features from the source domain data and the target domain data respectively, to obtain source domain data features and target domain data features; Inputting the source domain data features into the balanced classifier and the standard classifier respectively to obtain a source domain balanced classification loss and a source domain standard classification loss; Inputting the target domain data features into a standard classifier to generate a target domain data pseudo label for a balanced classifier; combining the original output of the standard classifier of the target domain data features with the output of the standard classifier of the target domain data features after post-logit adjustment to obtain an adjusted perception weight; Using the adjusted perception weights and the target domain pseudo labels, a target domain classification loss of a balanced classifier is obtained; and filtering the original output of the standard classifier of the target domain data features by using a confidence threshold to obtain the target domain classification loss of the standard classifier; The loss function is obtained by performing weighted calculation on the Coral loss, the source domain balanced classification loss, the source domain standard classification loss, the target domain classification loss of the balanced classifier, and the target domain classification loss of the standard classifier.

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