An unsupervised multi-source information domain adaptive method, device, medium and product for rotating machinery migration diagnosis

Abstract

This application discloses an unsupervised multi-source information domain adaptation method, device, medium, and product for rotating machinery migration diagnosis, relating to the field of mechanical fault diagnosis. The method comprises: acquiring multi-source information; fusing and compressing the multi-source information into a single fused sample; constructing a domain-adaptive self-attention network transfer learning model enhanced by information fusion; and inputting the single fused sample into the domain-adaptive self-attention network transfer learning model to obtain a migration diagnosis result for the rotating machinery. This application can address the problems of negative transfer, information loss, and high computational burden that exist in existing multi-source information domain adaptation methods.

Description

Technical Field

[0001] The present application relates to the field of mechanical fault diagnosis, and in particular to an unsupervised multi-source information domain adaptive method, device, medium and product for rotating machinery migration diagnosis. Background Art

[0002] Fault diagnosis is considered one of the key technologies for ensuring the safe and reliable operation of rotating machinery. Deep learning-based fault diagnosis methods, leveraging the advantages of adaptive feature extraction, have been extensively studied over the past decade. However, due to limitations such as insufficient labeled samples and the requirement that training and test samples must follow the same distribution, deep learning-based fault diagnosis methods have not yet been effectively and widely adopted in practical industrial applications.

[0003] Transfer diagnosis methods based on unsupervised domain adaptation can overcome the inability of deep learning-based fault diagnosis methods to accurately diagnose problems caused by insufficient labeled samples and diverse sample distributions. Despite their significant success in transfer diagnosis, unsupervised domain adaptation methods still have limitations. For one thing, focusing solely on knowledge from a single source domain fails to fully cover the target domain, hindering the generalization of transfer diagnosis models, particularly in diagnosing tasks involving more complex fault modes and varying operating conditions. Furthermore, due to the large volume and complexity of the data, selecting an appropriate single source domain often relies on subjective experience.

[0004] Multi-source domain adaptation helps address the limitations of the aforementioned research by leveraging data from multiple related but distinct domains. In recent years, multi-source domain adaptation has provided new insights into transfer diagnosis research, significantly improving diagnostic versatility under different operating conditions. The success of multi-source domain adaptation methods in transfer diagnosis can be attributed to the alignment of multi-source features using single-source information (i.e., signals from the same sensor at different speeds). However, relying solely on single-source information still has flaws. The same fault mode may exhibit completely different signal characteristics under different operating conditions, making single-source information more incomplete than multi-source information (e.g., heterogeneous signals such as vibration and electrical signals, as well as vibration signals from different axes). Therefore, the performance of transfer diagnosis models will be limited in the presence of incomplete information. Although multi-source information domain adaptation can provide deeper insights into fault modes and significantly improve diagnostic performance by effectively utilizing features from multiple sensor sources, it has the following disadvantages:

[0005] 1) There is a problem of negative transfer of feature distribution between multiple source information domains and target domains.

[0006] 2) Implementing feature alignment across multiple source information domains may reduce the diagnostic ability of the transfer learning model, making the transfer diagnosis performance inferior to that of methods based on a single source information domain.

[0007] 3) Current multi-source information domain adaptation methods ignore the spatial relationship between sensors, which may lead to the loss of critical fault information.

[0008] 4) In the information fusion module, as the number of information sources increases, the amount of training data and the scale of the model structure will increase significantly, which will seriously affect the computational efficiency of migration diagnosis. Summary of the Invention

[0009] In order to address the above-mentioned shortcomings of the prior art, the present application provides an unsupervised multi-source information domain adaptive method, device, medium and product for rotating machinery migration diagnosis.

[0010] To achieve the above objectives, this application provides the following solutions:

[0011] In a first aspect, the present application provides an unsupervised multi-source information domain adaptation method for rotating machinery migration diagnosis, comprising:

[0012] Acquire multi-source information; multi-source information includes time series signals; time series signals are collected by multiple sensors; time series signals cover various health states and working conditions in the information fusion source domain and target domain;

[0013] fusing and compressing the multi-source information into a single fused sample;

[0014] Constructing an information fusion-enhanced domain adaptive self-attention network transfer learning model; the constructed information fusion-enhanced domain adaptive self-attention network transfer learning model includes a multi-source feature extractor, a domain adaptation module and a classifier;

[0015] The single fusion sample is input into the information fusion enhanced domain adaptive self-attention network transfer learning model to obtain a transfer diagnosis result of the rotating machinery.

[0016] Optionally, fusing and compressing the multi-source information into a single fused sample includes:

[0017] Applying principal component analysis to process the multi-source information to obtain first three principal components of the principal component analysis results;

[0018] A channel fusion mechanism is designed, and the first three principal components are fused using the designed channel fusion mechanism to obtain the single fused sample.

[0019] Alternatively, the designed channel fusion mechanism is expressed as:

[0020]

[0021] Where, represents the i-th sample of the first single-source information domain, represents the i-th sample of the second single-source information domain, represents the i-th sample of the third single-source information domain, S1′ represents the first single-source information domain, S2′ represents the second single-source information domain, S3′ represents the third single-source information domain, and R S Represents the information of the red channel value in the image sample, G S Represents the information of the green channel value in the image sample, B S Represents information about the blue channel value in an image sample, represents the i-th information fusion sample.

[0022] Optionally, a domain-adaptive self-attention network transfer learning model enhanced by information fusion is constructed, including:

[0023] Construct the initial multi-source feature extractor, initial domain adaptation module and initial classifier respectively;

[0024] Acquire multi-sensor data, fuse and compress the multi-sensor data to obtain information fusion samples of different health conditions in a source domain and information fusion samples of different health conditions in a target domain;

[0025] Divide the information fusion samples of different health conditions in the source domain and the information fusion samples of different health conditions in the target domain into a training set and a test set;

[0026] The initial multi-source feature extractor, initial domain adaptation module, and initial classifier are jointly trained using the training set, and the model performance is evaluated using the test set. During the training process, the feature distributions of the information fusion source domain and the target domain are gradually aligned through the optimization of the adversarial loss. The training process continues until the initial multi-source feature extractor and the initial domain adaptation module are combined to extract domain-invariant features, and the classifier achieves the desired accuracy in the target domain. This results in the multi-source feature extractor, domain adaptation module, and classifier.

[0027] Based on the combination of the multi-source feature extractor, the domain adaptation module and the classifier, an information fusion-enhanced domain adaptive self-attention network transfer learning model is formed.

[0028] Optionally, the multi-source feature extractor is a two-dimensional convolutional neural network based on a parameter-independent self-attention mechanism; the multi-source feature extractor consists of three two-dimensional convolutional layers, three maximum pooling layers, three modules based on a parameter-independent self-attention mechanism, and one fully connected layer;

[0029] The parameter-independent self-attention mechanism module is used to generate a feature weight map based on the feature maps output by three two-dimensional convolutional layers and three maximum pooling layers, and to obtain an enhanced feature map based on the feature weight map.

[0030] Optionally, the domain adaptation module includes: a domain discriminator module and a feature distribution difference measurement module;

[0031] The domain discriminator module determines feature weights based on test errors by introducing a data-level weight allocation mechanism; the feature distribution difference measurement module introduces conditional distribution alignment to achieve domain adaptation.

[0032] Optionally, the loss function of the domain discriminator module after the data-level weight distribution mechanism is introduced is expressed as L D (θ f ,θ d ):

[0033]

[0034] Where, represents the normalized weight of the i-th sample in the source domain, D(*) represents the domain discriminator, represents the features of the i-th sample in the source domain, represents the characteristics of the i-th sample in the target domain, θ d represents the training parameters of the domain discriminator module D, represents the number of batch samples in the source domain, Indicates the number of batch samples in the target domain;

[0035] The loss function of the feature distribution difference measurement module that introduces conditional distribution alignment is expressed as L J (θ f ,θ c ):

[0036]

[0037] Where, represents the predicted label of the i-th sample in the source domain, represents the predicted label of the jth sample in the target domain, k(·) represents the Gaussian kernel function, represents the predicted label of the source domain, represents the predicted label of the target domain, represents the jth feature of the source domain, represents the i-th feature of the target domain, represents the predicted label of the jth sample in the source domain, represents the predicted label of the i-th sample in the target domain.

[0038] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-described unsupervised multi-source information domain adaptation methods for rotating machinery migration diagnosis.

[0039] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned unsupervised multi-source information domain adaptation methods for rotating machinery migration diagnosis.

[0040] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned unsupervised multi-source information domain adaptation methods for rotating machinery migration diagnosis.

[0041] According to the specific embodiments provided in this application, this application has the following technical effects:

[0042] The present application provides an unsupervised multi-source information domain adaptation method, device, medium and product for migration diagnosis of rotating machinery. By fusing and compressing multi-source information into a single fusion sample, the fusion of multi-source information is achieved to solve the negative migration problem of the feature distribution between the multi-source information domain and the target domain. In addition, by fusing multi-source information formed by time series signals collected by different sensors, the spatial relationship between sensors is taken into account, and the problem of key fault information loss caused by ignoring the spatial relationship between sensors can be solved. By adopting the information fusion-enhanced domain adaptation attention network (IF-EDAAN) transfer learning model, the migration diagnosis result of the rotating machinery is obtained based on a single fusion sample, which can effectively reduce the computational burden and improve the fault diagnosis performance, thereby solving the problems of high computational burden and so on in multi-source information domain adaptation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 This is a diagram of an application environment of an unsupervised multi-source information domain adaptation method for rotating machinery migration diagnosis in one embodiment of the present application;

[0045] Figure 2 A flowchart of an unsupervised multi-source information domain adaptation method for rotating machinery migration diagnosis provided by one embodiment of the present application;

[0046] Figure 3 A schematic diagram of the structure of the information fusion enhanced domain adaptive self-attention network transfer learning model provided in one embodiment of the present application;

[0047] Figure 4 A schematic diagram of the architecture design of a PFAM module provided in another embodiment of the present application;

[0048] Figure 5 This is an overall implementation flow chart of the unsupervised multi-source information domain adaptation method for rotating machinery migration diagnosis provided by one embodiment of the present application;

[0049] Figure 6 A schematic diagram of convergence curves of different methods on the information fusion target domain test set in the T8→T3 migration diagnosis scenario provided by one embodiment of the present application;

[0050] Figure 7 A schematic diagram of the computational time and memory consumption results of different unsupervised transfer learning methods provided in another embodiment of the present application.

[0051] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] The unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send multi-source information to the server 104. After the server 104 receives the multi-source information, the server 104 fuses and compresses the multi-source information into a single fusion sample. Construct an information fusion enhanced domain adaptive self-attention network transfer learning model. Input the single fusion sample into the information fusion enhanced domain adaptive self-attention network transfer learning model to obtain the migration diagnosis result of the rotating machinery. Among them, the constructed information fusion enhanced domain adaptive self-attention network transfer learning model includes a multi-source feature extractor, a domain adaptation module and a classifier. The server 104 can feedback the obtained migration diagnosis result to the terminal 102. In addition, in some embodiments, the unsupervised multi-source information domain adaptation method for migration diagnosis of rotating machinery can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform migration diagnosis on the multi-source information, or the server 104 can obtain the multi-source information from the data storage system and perform migration diagnosis on the multi-source information.

[0055] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0056] In an exemplary embodiment, Figure 2 As shown, an unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 201 to 203.

[0057] Step 200: Acquire multi-source information. The multi-source information includes time series signals. The time series signals are collected by multiple sensors. The time series signals cover various health states and working conditions in the information fusion source and target domains.

[0058] Step 201: Fuse and compress multi-source information into a single fused sample.

[0059] Step 202: Construct a domain adaptive self-attention network transfer learning model enhanced by information fusion. Figure 3 As shown in the figure, the constructed information fusion enhanced domain adaptive self-attention network transfer learning model includes a multi-source feature extractor, a domain adaptation module and a classifier.

[0060] Step 203: Input the single fusion sample into the information fusion enhanced domain adaptive self-attention network transfer learning model to obtain the transfer diagnosis result of the rotating machinery.

[0061] Implementing the above steps 200 to 203 can solve the problems of negative transfer, information loss, and high computational burden that exist in the known multi-source information domain adaptation methods.

[0062] In another exemplary embodiment of the present application, in order to mine spatial information, it is necessary to fuse and compress multi-source information into a single fused sample. Based on this, in this embodiment, the implementation process of the above step 201 is as follows: First, principal component analysis (PCA) is applied to process multi-source information from different sensors. In order to obtain three RGB (red, green, blue) channel values, only the first three principal components are retained. Then, the enhanced signal is randomly sampled and normalized. Again, information fusion is performed based on the first three principal components to compress multi-source data and enhance fault characteristics.

[0063] In actual application, considering the spatial relationship between multiple sensors, a channel fusion mechanism is also designed in this embodiment to fuse the first three principal components using the designed channel fusion mechanism to obtain a single fused sample. The designed channel fusion mechanism is expressed as:

[0064]

[0065] Where, represents the i-th sample of the first single-source information domain, represents the i-th sample of the second single-source information domain, represents the i-th sample of the third single-source information domain, S1′ represents the first single-source information domain, S2′ represents the second single-source information domain, S3′ represents the third single-source information domain, and R S Represents the information of the red channel value in the image sample, G S Represents the information of the green channel value in the image sample, B S Represents information about the blue channel value in an image sample, represents the i-th information fusion sample.

[0066] In another exemplary embodiment of the present application, the process of constructing the information fusion enhanced domain adaptive self-attention network transfer learning model in the above step 202 can be described as follows:

[0067] (1) An initial multi-source feature extractor, an initial domain adaptation module, and an initial classifier are constructed respectively to initialize the information fusion-enhanced domain adaptive self-attention network.

[0068] (2) Acquire multi-sensor data, fuse and compress the multi-sensor data, and obtain information fusion samples of different health conditions in the source domain and information fusion samples of different health conditions in the target domain.

[0069] (3) The information fusion samples of different health conditions in the source domain and the information fusion samples of different health conditions in the target domain are divided into a training set and a test set.

[0070] Among them, the method of fusing and compressing multi-sensor data refers to the fusion method of multi-source information mentioned above. By fusing and compressing multi-sensor data, information fusion samples of different health conditions from the source domain and the target domain can be similarly obtained. On this basis, the multi-source information fusion source domain (the domain with labels for training the model) and the target domain (the domain without labels that requires the model to make predictions) are divided to obtain training sets and test sets, and the obtained training sets and test sets are used as data input for the information fusion-enhanced domain adaptation attention network (IF-EDAAN) transfer learning model proposed in the above application.

[0071] (4) The initial multi-source feature extractor, initial domain adaptation module, and initial classifier are jointly trained using the training set, and the model performance is evaluated using the test set. During the training process, the feature distributions of the information fusion source domain and the target domain are gradually aligned through the optimization of the adversarial loss. The training process continues until the initial multi-source feature extractor and the initial domain adaptation module jointly extract domain-invariant features and the classifier achieves the desired accuracy in the target domain. The resulting multi-source feature extractor, domain adaptation module, and classifier can effectively adapt to the information fusion target domain, achieving seamless feature migration and accurate classification across domains.

[0072] (5) A domain-adaptive self-attention network transfer learning model with enhanced information fusion is formed based on the combination of multi-source feature extractor, domain adaptation module and classifier.

[0073] In another exemplary embodiment of the present application, the above step (1) is essentially to initialize the information fusion enhanced domain adaptive self-attention network. Based on this, this implementation process can refer to the following steps.

[0074] 1) Construct an initial multi-source feature extractor. In this embodiment, the present application designs a two-dimensional convolutional neural network (CNN) based on a parameter-free attention mechanism (PFAM) as an initial multi-source feature extractor (PFAM-CNN). Specifically, the initial multi-source feature extractor consists of three two-dimensional convolutional layers, three maximum pooling layers, three PFAM modules and a fully connected layer (FC). As shown in the attached figure, Figure 3 As shown in the figure, in the multi-source feature extractor finally constructed, Conv1&BatchNorm&ReLU, Conv2&BatchNorm&ReLU and Conv3&BatchNorm&ReLU represent three two-dimensional convolutional layers, MaxPool2D represents the maximum pooling layer, PFAM module represents the PFAM module, and Stride represents the stride. The PFAM module receives the feature maps generated by the previous convolutional layer and pooling layer. The architecture design of the PFAM module is shown in the figure. Figure 4 As shown in Figure 2, it is assumed that the feature map (denoted as f) generated by the previous convolutional layer and pooling layer is the input of the PFAM module, where C represents the number of channels, W and H represent the width and height of the feature map respectively.

[0075] First, calculate the mean m of each channel c in the feature map f c and variance have:

[0076]

[0077] Among them, f c,i,j Represents the original eigenvalue of channel c at position index (i, j).

[0078] Then, generate the feature weight map, which includes:

[0079]

[0080] Where c is the channel index, i∈{1,2,...,H} is the index in the height dimension, j∈{1,2,...,W} is the index in the width dimension, ε is a small constant used to avoid the uncertainty of division by zero, and y c,i,j Represents the feature weight of channel c at position index (i, j).

[0081] Finally, we get the enhanced feature map It is described as follows:

[0082] α=σ(y).

[0083]

[0084] Among them, y represents the feature weight map of all channels and spatial dimensions, s(·) represents the sigmoid activation function, and α represents the enhancement coefficient of the feature map f.

[0085] By generating a feature weight map, the two-dimensional PFAM-CNN proposed in this application can effectively weight features across different spatiotemporal dimensions, thereby focusing more attention on important domain-invariant features while suppressing noise and irrelevant features. This adaptive feature weight adjustment method using the PFAM module can effectively optimize the feature results of information fusion, thereby improving the effectiveness and accuracy of domain-invariant feature extraction. Finally, PFAM-CNN extracts the features F of the information-fused source and target domains, as shown below:

[0086] F=Concatenate(F S ,F T )=G((D S ,D T ),θ f ).

[0087] Among them, θ f represents the training parameters of the multi-source feature extractor proposed in this application, D S and D T are the input samples of the information fusion source domain and target domain respectively. S and F T Representative D S and D T The features of , G(·) represents the multi-source feature extractor.

[0088] 2) Construct an initial domain adaptation module. The constructed initial domain adaptation module includes the domain discriminator module D and the feature distribution difference measurement module joint maximum mean discrepancy measurement method (JMMD). Among them:

[0089] a) Domain discriminator module.

[0090] Considering the impact of the differences between information fusion source domain samples on transfer diagnosis, this application proposes a data-level weight allocation mechanism. This mechanism assumes that the domain discriminator module D has difficulty in distinguishing samples with high similarity between the information fusion source domain and the target domain, but is easier to distinguish samples with large differences. This weight allocation mechanism aims to redefine the contribution of each sample in transfer diagnosis. The weight of each sample in the information fusion source domain is determined by the domain discriminator module D based on the domain prediction error. Calculated, where Represents the characteristics of the i-th sample in the source domain. Based on this, we have:

[0091]

[0092]

[0093] in, and represents the weight and normalized weight of the i-th sample in the information fusion source domain, m S and s S Denote the mean and standard deviation of the source domain weight set, respectively. Specifically, the domain discriminator module D consists of three FC layers. During training using the source domain dataset, this weight distribution mechanism forces the model to pay more attention to samples similar to the target domain, thereby promoting better transfer diagnosis in the target domain.

[0094] After introducing the above-mentioned sample weight distribution mechanism, the loss function L of the domain discriminator module D is D (θ f ,θ d ) is represented as follows:

[0095]

[0096] Among them, represents the normalized weight of the i-th sample in the source domain, D(*) represents the domain discriminator, represents the features of the i-th sample in the source domain, represents the characteristics of the i-th sample in the target domain, θ d represents the training parameters of the domain discriminator module D, represents the number of batch samples in the source domain, Indicates the number of batch samples of the target domain.

[0097] b) Feature distribution difference measurement module.

[0098] A distance metric-based method, JMMD, is used to facilitate the extraction of domain-invariant spatiotemporal features. This method maximizes and more effectively transfers knowledge gained from the information fusion source domain to the information fusion target domain. JMMD introduces conditional distribution alignment to achieve more effective domain adaptation. Based on this, the loss function of the feature distribution difference measurement module that introduces conditional distribution alignment is expressed as:

[0099]

[0100] Among them, represents the predicted label of the i-th sample in the source domain, represents the predicted label of the jth sample in the target domain, k(·) represents the Gaussian kernel function, represents the predicted label of the source domain, represents the predicted label of the target domain, represents the jth feature of the source domain, represents the i-th feature of the target domain, represents the predicted label of the jth sample in the source domain, represents the predicted label of the i-th sample in the target domain.

[0101] 3) Classifier.

[0102] For the classifier module composed of FC layers, it can output the diagnostic accuracy of the information fusion source domain and the information fusion target domain, which are:

[0103] P i =Softmax(a i ·F i ′+o i ).

[0104] Among them, P i is the category prediction probability of the information fusion source domain or target domain sample, Softmax(·) represents the softmax classifier, θ c =(a i ,o i ) is the training parameter of the classifier, F i ′ is the feature of the i-th sample from the information fusion source domain or target domain. Given a sample x i The predicted probability P i , its predicted class index in the information fusion source domain or target domain is the corresponding vector P i,j The position index with the highest probability is expressed as follows:

[0105]

[0106] Among them, P i,j Represents sample x i The probability of being predicted as category j in the information fusion source domain or target domain.

[0107] Therefore, the average diagnostic accuracy A is expressed as:

[0108]

[0109] Among them, y i Represents the sample x in the source domain or target domain i The true label, I(·) represents the indicator function, N b Represents the number of samples in a batch of information fusion source or target domains.

[0110] In addition, the classifier loss L of the information fusion source domainC (θ f ,θ c )The expression is as follows:

[0111]

[0112] in, It is a sample The probability of being predicted as category j in the information fusion source domain, It is a sample The true label. is the total number of categories.

[0113] In another exemplary embodiment of the present application, the initial IF-EDAAN transfer learning model constructed above is trained. This training process includes: training an initial multi-source feature extractor (denoted as G) to extract domain-invariant features. Training an initial domain adaptation module to achieve domain-invariant feature alignment. Training an initial classifier (denoted as C) to output the diagnostic accuracy of the information fusion target domain transfer diagnosis training set.

[0114] In the training process of the IF-EDAAN transfer learning model, the goal of domain adaptation is to optimize the initial multi-source feature extractor G to extract domain-invariant spatiotemporal features F, so that the classifier C can achieve accurate migration diagnosis. The loss function of the proposed IF-EDAAN transfer learning model is expressed as L(θ f ,θ c ,θ d ):

[0115] L(θ f ,θ c ,θ d )=L C (θ f ,θ c )-λL D (θ f ,θ d )+μL J (θ f ,θ c ).

[0116]

[0117] Where λ and μ are regularization coefficients, t represents the current iteration time during training, K represents the control change rate, and m represents the maximum value of the regularization coefficient.

[0118] Finally, the parameters θ of the IF-EDAAN transfer learning model f ,θ d and θ c The optimization update will be performed as follows:

[0119]

[0120] Among them, b is the initial learning rate of the model, N p is the total number of training cycles, is the current training cycle number, θ f ′ is the updated parameter θ f ,θ c ′ is the updated parameter θ c ,θ d ′ is the updated parameter θ d ,← represents optimization update, L C is the classifier loss function L of the information fusion source domain C (θ f ,θ c ) is a simplified representation of L D is the loss function L of the domain discriminator module D (θ f ,θ d ), LJ is the loss function L of the feature distribution difference measurement module that introduces conditional distribution alignment J (θ f ,θ c ). The initial learning rate b can be adjusted during training according to the following formula:

[0121]

[0122] Finally, the trained initial IF-EDAAN transfer learning model is tested. Specifically, after the initial IF-EDAAN transfer learning model is trained, the multi-source information fusion test set from the source and target domains will be input into the trained IF-EDAAN model. These sets will only pass through the multi-source feature extractor and classifier modules before the classifier outputs the diagnostic classification results.

[0123] Based on the above description, in practical applications, this application first uses multiple sensors to collect raw time series signals from rotating machinery, covering various health states and operating conditions in the information fusion source and target domains. Next, information fusion is performed. Before information fusion, principal component analysis is used to compress and enhance the raw time series signals collected from multiple sensors. The compressed and enhanced signals are then randomly sampled and normalized. The compressed and enhanced samples are then aligned with the three color channels to form image samples based on multi-source information fusion. Training and test sets are then divided into the multi-source information fusion source and target domains. Next, a domain-adaptive self-attention network transfer learning model enhanced by information fusion is constructed. To construct this domain-adaptive self-attention network transfer learning model, a multi-source feature extractor, a domain adaptation module, and a classifier are constructed separately. Finally, the domain-adaptive self-attention network transfer learning model enhanced by information fusion is trained. The information fusion images from the source and target domains are input to the multi-source feature extractor. By learning feature weights, domain-invariant spatiotemporal features of the information fusion samples are effectively extracted. Domain adaptation and classification are then performed. By training the domain discriminator, the feature distribution difference between the information fusion source domain and the information fusion target domain is reduced, further promoting the multi-source feature extractor to extract domain-invariant spatiotemporal features. At the same time, by minimizing the classification loss in the source domain, the accuracy of the classifier in the information fusion source domain can be improved, and excellent migration diagnosis accuracy in the information fusion target domain can be achieved. Finally, the migration diagnosis result is output. The multi-source information fusion test set of the source domain and the target domain will be input into the trained IF-EDAAN transfer learning model, and they will only pass through the multi-source feature extractor and classifier, and the classifier will output the final diagnostic classification result. This application fully considers the impact of different working conditions on diagnosis in industrial scenarios, provides a new method for fault diagnosis, and can provide important technical support for the safe and reliable operation of equipment.

[0124] Further, if Figure 5 In the illustrated embodiment, it should be noted that in the IF-EDAAN transfer learning model, MK-MMD represents the multi-kernel maximum mean difference metric, JMMD represents the joint maximum mean difference metric, S-DANN represents the domain adversarial neural network based on a single-source information domain, M-DANN represents the domain adversarial neural network based on multiple-source information domains, and DAG-MDAN represents the intra-adversarial guided unsupervised multi-domain adaptation network. The diagnostic accuracy of the proposed IF-EDAAN transfer learning model was evaluated in six different transfer diagnosis scenarios. Figure 6The convergence curves of different methods on the information fusion target domain test set in the T8→T3 migration diagnosis scenario are shown. The IF-EDAAN transfer learning model proposed in this application converges the fastest and most stably, with the highest accuracy of 98.6%. The best accuracy of the DAG-MDAN method is 78.95%, which is still 19.65% lower than the proposed IF-EDAAN transfer learning model. It should be emphasized that the M-DANN method can achieve stable convergence. However, the accuracy of the M-DANN method is poor, with the best accuracy of only 62.05%. These results show that compared with other unsupervised transfer learning methods, the method proposed in this application has stronger stability and better convergence performance.

[0125] like Figure 7 The computational time and memory consumption results of different unsupervised transfer learning methods are shown. By comparing the computational time of the MK-MMD, JMMD, S-DANN, M-DANN, and DAG-MDAN methods with the method proposed in this application, it can be seen that the total computational time required by this application is the shortest, at only 182.71 seconds. One reason for this result is that information fusion can both compress and enhance multi-sensor vibration signals, enabling the IF-EDAAN transfer learning model to process multi-source information simultaneously. Another possible reason is that it can better accelerate image processing efficiency. In comparison, the DAG-MDAN method requires the most computational time, a total of 1300.73 seconds. These findings indicate that the method provided in this application is more computationally efficient than other unsupervised transfer learning methods. The method proposed in this application requires the least memory, a total of 394.1MB. This is because information fusion compresses three vibration signal samples into one image sample. Compared to the results of MK-MMD, JMMD, and S-DANN using single-source domain input, their memory requirements are 1425.6MB, 1031.5MB more than the method proposed in this application. This is because different network structure complexities lead to different memory usage. It is important to emphasize that the M-DANN method consumes the most memory, reaching 2253.05MB. These findings indicate that compared with other unsupervised transfer learning methods, the method proposed in this application has a more optimized network structure.

[0126] Furthermore, in order to verify the diagnostic capability of the method proposed in this application, an ablation experiment was conducted. Table 1 lists the ablation experiment results under various migration conditions. IF-EDAAN (without PFAM module), IF-DAAN (without weight distribution module), EDAAN (without IF module) and F-EDAAN (without PCA module). The method proposed in this application is superior to the methods of IF-EDAAN (without PFAM module), IF-DAAN (without weight distribution module), EDAAN (without IF module) and F-EDAAN (without PCA module). In different migration scenarios, the average diagnostic accuracy is the highest, reaching 98.88%, which is an increase of 1.89%, 1.97%, 12.81% and 2.93% respectively. Under the condition of changes in speed and current load, the method proposed in this application can significantly improve the diagnostic accuracy, which is reflected in the migration scenarios of T8→T3 and T8→T1. The method provided in this application significantly improves the average diagnostic accuracy, by 29.88% and 39.48% respectively. This improvement is due to the method proposed in this application, which significantly expands and enhances diagnostic knowledge in complex migration scenarios. In contrast, in the simple migration scenarios of T5→T6 and T4→T3, the accuracy of the method proposed in this application still achieved improvements of 0.48% and 0.56%, respectively. Comparing the diagnostic accuracy of the method proposed in this application with that of IF-DAAN alone, the proposed method achieved improvements of 7.56%, 1.2%, 1.48%, 0.08%, 0.4%, and 0.6%, respectively, in various migration scenarios. This is because the PFAM module employed in this application captures correlations between multi-source sensor data and adaptively increases the weight of domain-invariant features, effectively improving the performance of migration diagnosis. The domain adaptation module improves training stability, as demonstrated in the migration scenarios of T8→T3, T8→T1, T7→T1, and T4→T8. Furthermore, the application of PCA effectively retains key fault features and removes redundant features, thereby improving diagnostic accuracy.

[0127] Table 1 Ablation experiment results under various migration conditions

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[0130] In summary, compared with the prior art, this application has the following advantages:

[0131] 1. This application proposes an unsupervised multi-source information domain adaptation method, which fuses and compresses multi-source data into a unified representation through principal component analysis and RGB channel fusion. It can effectively enrich the domain-invariant features under different working conditions, improve performance by mining potential spatial information from multiple sensors through RGB channel fusion, and realize migration diagnosis between information fusion source domain and target domain.

[0132] 2. This application proposes an IF-EDAAN transfer learning model architecture that fuses and compresses multi-source data into a unified two-dimensional representation. This approach effectively eliminates potential conflicts between multi-sensor data and prevents negative transfer problems.

[0133] 3. This application designs a PFAM module within the IF-EDAAN transfer learning model, which enhances multi-source feature extraction by assigning feature weights at the feature level. Furthermore, based on the weight assignment mechanism, the transfer diagnosis performance of the target domain can be improved.

[0134] 4. This application designs a parameter-independent self-attention mechanism module in the multi-source feature extractor, which can redefine the weights of information fusion samples at the feature level, so that important domain-invariant features receive more attention while suppressing noise and irrelevant features.

[0135] 5. This application proposes a domain adaptation module and a joint maximum mean difference measurement strategy, which can effectively extract and align domain-invariant temporal and spatial features, focus on enhancing discriminative features, and improve migration diagnosis performance.

[0136] 6. The two-dimensional data compression and fusion method proposed in this application can also greatly improve the performance utilization of the graphics processing unit (GPU), thereby reducing the computational burden and improving the training efficiency of the proposed transfer learning model.

[0137] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store migration diagnostic data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis is implemented.

[0138] Those skilled in the art will understand that Figure 8 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0139] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0140] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0141] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0142] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0143] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0144] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An unsupervised multi-source information domain adaptation method for rotating machinery migration diagnosis, characterized in that: The unsupervised multi-source information domain adaptation method for rotating machinery migration diagnosis includes: Acquire multi-source information; the multi-source information includes time series signals; the time series signals are collected by multiple sensors; the time series signals cover various health states and working conditions in the information fusion source domain and the target domain; fusing and compressing the multi-source information into a single fused sample; Constructing an information fusion-enhanced domain adaptive self-attention network transfer learning model; the constructed information fusion-enhanced domain adaptive self-attention network transfer learning model includes a multi-source feature extractor, a domain adaptation module and a classifier; Inputting the single fusion sample into the information fusion enhanced domain adaptive self-attention network transfer learning model to obtain a transfer diagnosis result of the rotating machinery; Construct a domain-adaptive self-attention network transfer learning model enhanced by information fusion, including: Construct the initial multi-source feature extractor, initial domain adaptation module and initial classifier respectively; Acquire multi-sensor data, fuse and compress the multi-sensor data to obtain information fusion samples of different health conditions in a source domain and information fusion samples of different health conditions in a target domain; Divide the information fusion samples of different health conditions in the source domain and the information fusion samples of different health conditions in the target domain into a training set and a test set; The initial multi-source feature extractor, initial domain adaptation module, and initial classifier are jointly trained using the training set, and the model performance is evaluated using the test set. During the training process, the feature distributions of the information fusion source domain and the target domain are gradually aligned through the optimization of the adversarial loss. The training process continues until the initial multi-source feature extractor and the initial domain adaptation module jointly extract domain-invariant features and the classifier achieves the desired accuracy in the target domain. The multi-source feature extractor, domain adaptation module, and classifier are then obtained. Based on the combination of the multi-source feature extractor, the domain adaptation module and the classifier, an information fusion-enhanced domain adaptive self-attention network transfer learning model is formed; The multi-source feature extractor is a two-dimensional convolutional neural network based on a parameter-independent self-attention mechanism; the multi-source feature extractor is composed of three two-dimensional convolutional layers, three maximum pooling layers, three parameter-independent self-attention mechanism modules, and a fully connected layer; the parameter-independent self-attention mechanism module is used to generate a feature weight map based on the feature maps output by the three two-dimensional convolutional layers and the three maximum pooling layers, and to obtain an enhanced feature map based on the feature weight map; The feature map f generated by the convolution layer and the pooling layer is used as the input of the parameter-independent self-attention mechanism module, and the mean μ of each channel c in the feature map f is calculated. c and variance have: Where, f c,i,j Represents the original eigenvalue of channel c at position index (i, j), W and H represent the width and height of the feature map respectively; Then, a feature weight map is generated; among them, there are: Where c is the channel index, i∈{1,2,...,H} is the index in the height dimension, j∈{1,2,...,W} is the index in the width dimension, ε is a small constant used to avoid the uncertainty of division by zero, and y c,i,j Represents the feature weight of channel c at position index (i, j); Finally, the enhanced feature map z is obtained, which is described as follows: α=σ(y); z=f·α; Among them, y represents the feature weight map of all channels and spatial dimensions, σ(·) represents the sigmoid activation function, and α represents the enhancement coefficient of the feature map f.

2. The unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis according to claim 1 is characterized in that: The multi-source information is fused and compressed into a single fused sample, including: Applying principal component analysis to process the multi-source information to obtain first three principal components of the principal component analysis results; A channel fusion mechanism is designed, and the first three principal components are fused using the designed channel fusion mechanism to obtain the single fused sample.

3. The unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis according to claim 2, characterized in that: The designed channel fusion mechanism is expressed as: Where, represents the i-th sample of the first single-source information domain, represents the i-th sample of the second single-source information domain, represents the i-th sample of the third single-source information domain, S1′ represents the first single-source information domain, S2′ represents the second single-source information domain, S3′ represents the third single-source information domain, and R S Represents the information of the red channel value in the image sample, G S Represents the information of the green channel value in the image sample, B S Represents information about the blue channel value in an image sample, represents the i-th information fusion sample.

4. The unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis according to claim 1, characterized in that: The domain adaptation module includes: a domain discriminator module and a feature distribution difference measurement module; The domain discriminator module determines feature weights based on test errors by introducing a data-level weight allocation mechanism; the feature distribution difference measurement module introduces conditional distribution alignment to achieve domain adaptation.

5. The unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis according to claim 4, characterized in that: The loss function of the domain discriminator module after the introduction of the data-level weight distribution mechanism is expressed as L D (θ f ,θ d ): Where, represents the normalized weight of the i-th sample in the source domain, D(*) represents the domain discriminator, and F i S represents the features of the i-th sample in the source domain, represents the feature of the jth sample in the target domain, θ f represents the training parameters of the multi-source feature extractor, θ d represents the training parameters of the domain discriminator module D, represents the number of batch samples in the source domain, Indicates the number of batch samples in the target domain; The loss function of the feature distribution difference measurement module that introduces conditional distribution alignment is expressed as L J (θ f ,θ c ): Where, represents the predicted label of the i-th sample in the source domain, represents the predicted label of the jth sample in the target domain, k(·) represents the Gaussian kernel function, represents the predicted label of the source domain, represents the predicted label of the target domain, represents the jth feature of the source domain, F i T represents the i-th feature of the target domain, represents the predicted label of the jth sample in the source domain, represents the predicted label of the i-th sample in the target domain.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the unsupervised multi-source information domain adaptation method for rotating machinery migration diagnosis according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis according to any one of claims 1 to 5 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the unsupervised multi-source information domain adaptive method for rotating machinery migration diagnosis according to any one of claims 1 to 5 is implemented.

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