A bearing fault diagnosis method based on time-varying working condition transfer learning based on diffusion model

Abstract

The present invention provides a method for diagnosing bearing faults using time-varying working condition transfer learning based on a diffusion model, comprising: obtaining the original bearing fault vibration signal and preprocessing it to obtain a two-dimensional time-spectrum image; constructing a denoising diffusion model so that the denoising diffusion model learns the image features of the two-dimensional time-spectrum image under stable working conditions; constructing a complete bearing fault dataset under stable working conditions based on the vibration signal under time-varying working conditions using a repaint method for the two-dimensional time-spectrum image; and performing bearing fault diagnosis using the expanded bearing fault dataset and a domain generalization method. The present invention solves the problem of low bearing fault diagnosis accuracy caused by inconsistent data distribution in domain generalization bearing fault diagnosis, improves the accuracy of bearing fault diagnosis, and is more adaptable to bearing fault diagnosis in different bearing application scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed train bearing fault diagnosis, and in particular to a bearing fault diagnosis method based on time-varying working condition transfer learning of a diffusion model. Background Art

[0002] Intelligent fault diagnosis of rotating machinery plays a vital role in industrial production. High-speed train bogies, as a crucial component of the vehicle, bear the train's weight and ensure smooth operation. Therefore, timely and effective condition monitoring and fault diagnosis are crucial for ensuring safe train operation. However, current condition monitoring and fault diagnosis methods face numerous challenges in processing massive amounts of monitoring data. High-speed trains generate a vast amount of monitoring data during operation, covering a wide range of operating conditions. However, existing diagnostic models often suffer from limited generalization capabilities when processing this complex data, making it difficult to maintain high-precision diagnostic capabilities under a wide range of operating conditions. This limits the practical application and reliability of fault diagnosis systems. Furthermore, the labeled data contained in this massive amount of monitoring data is often scarce and concentrated on specific operating conditions, further complicating fault diagnosis. Therefore, how to complete fault diagnosis tasks with limited available data is a pressing issue for intelligent fault diagnosis.

[0003] To address the need for intelligent fault diagnosis under time-varying operating conditions in scenarios where sample data is scarce, a direct and effective approach is to employ domain transfer technology. Currently, mainstream intelligent diagnosis solutions for time-varying operating conditions employ domain generalization methods. However, domain generalization methods require valid fault diagnosis data, and limited data makes it difficult to train effective domain generalization models.

[0004] The denoising diffusion model has become a highly efficient and effective method for generating data due to its excellent performance and training stability. The images generated by the denoising diffusion model are based on the trained images. While the data used in bearing fault diagnosis is more stable, in industrial applications, more data is time-varying. This results in low accuracy in bearing fault diagnosis due to inconsistent data distribution in domain generalization models. Therefore, it is necessary to target the data characteristics of stable conditions, allowing data generated under time-varying conditions to be based on stable conditions, thereby improving the accuracy of domain-generalized bearing fault diagnosis. Therefore, it is necessary to develop a model that allows a small amount of time-varying condition data to learn from stable condition data based on its current state, thereby addressing the low fault diagnosis accuracy caused by inconsistent data distribution in domain generalization. Summary of the Invention

[0005] The purpose of the present invention is to address the deficiencies in the above-mentioned background technology and provide a generalized bearing fault diagnosis method for time-varying working conditions. When the time-varying working condition data is limited, the method generates a bearing fault diagnosis signal under stable working conditions based on some characteristics of the time-varying working conditions, thereby solving the problem of low fault diagnosis accuracy caused by inconsistent distribution of generalized bearing fault diagnosis data.

[0006] To achieve the above objectives, the present invention provides a bearing fault diagnosis method based on time-varying working condition transfer learning of a diffusion model, comprising the following steps:

[0007] S1, obtain the original fault vibration signal of the bearing and preprocess it to obtain a two-dimensional time-frequency spectrum image;

[0008] S2, constructing a denoising diffusion model, placing the acquired two-dimensional time-frequency spectrum image of the stable working condition into the denoising diffusion model for model training, so that the denoising diffusion model learns the image features of the two-dimensional time-frequency spectrum image of the stable working condition;

[0009] S3: Using the Repaint method of a two-dimensional time-spectrum image, the vibration signal of the time-varying working condition is masked according to the established masking rules. Based on the unmasked portion of the vibration signal of the unstable working condition and the image features learned by the denoising diffusion model, the Repaint method generates a continuous stable vibration signal that conforms to the vibration signal law from the masked area, thus constructing a complete bearing fault dataset under stable working conditions.

[0010] S4, uses the bearing fault dataset expanded by S3 and the domain generalization method to perform bearing fault diagnosis.

[0011] Furthermore, the bearing fault vibration signal is collected in S1, and the one-dimensional vibration signal is converted into a two-dimensional time-frequency spectrum image using time-frequency analysis of complex morlet wavelet transform.

[0012] Furthermore, S2 includes the following sub-steps:

[0013] S21, the two-dimensional time-frequency spectrum image x0 of the stable working condition is input into the denoising diffusion model. At each step t, the denoising diffusion model adds Gaussian noise to the two-dimensional time-frequency spectrum image using the following formula, so that the two-dimensional time-frequency spectrum image becomes a Gaussian white noise image with independent distributions:

[0014]

[0015] Where t represents the step size, t=1,2,...,T; α t represents the adjustment parameter of the tth step, which decreases as the step length increases; ε t It follows a standard normal distribution Sampling noise;

[0016] S22, gradually denoise the independently distributed Gaussian white noise image and restore it to a two-dimensional time-frequency spectrum image similar to the original image. T Initially, step-by-step noise removal is performed at each step t, as shown below:

[0017]

[0018] Among them, ε θ (x t ,t) is the prediction noise learned by S21; σ t is the adjustment parameter;

[0019] S23, repeating S21 to S22 multiple times to obtain a denoising diffusion model capable of generating a two-dimensional time-frequency spectrum image of a stable working condition, and saving the denoising diffusion model.

[0020] Furthermore, S3 includes the following sub-steps:

[0021] S31, longitudinally masking the two-dimensional time-frequency spectrum image of the time-varying working condition;

[0022] S32, generating a stable two-dimensional time-frequency spectrum image of the masked area by the Repaint method;

[0023] S33, repeating S32 multiple times until the generated image includes a complete two-dimensional time-frequency spectrum image of a stable working condition, in which the image of the Repaint area is fused with the original image.

[0024] Furthermore, in S31 , the left 1 / 8 position of the two-dimensional time-frequency spectrum image of the time-varying working condition is retained, and the right side is covered with a black image.

[0025] Furthermore, S4 includes the following sub-steps:

[0026] S41, using the bearing fault data of the stable working condition at low speed and the generated bearing fault data of the stable working condition as the source domain, and the bearing fault data of the stable working condition at high speed as the target domain of domain generalization;

[0027] S42, using the domain generalization method to train the model based on the source domain data, perform bearing fault diagnosis on the target domain data, output the accuracy of the bearing fault diagnosis, and determine the bearing fault condition.

[0028] Furthermore, domain generalization methods include MMD, Mixup, ERM, DANN and ANDMask methods.

[0029] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for diagnosing bearing faults by time-varying working condition transfer learning based on a diffusion model.

[0030] The present invention also provides an electronic device comprising at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a bearing fault diagnosis method based on time-varying working condition transfer learning of a diffusion model as described above.

[0031] The above solution of the present invention has the following beneficial effects:

[0032] The bearing fault diagnosis method based on time-varying working condition transfer learning of the diffusion model provided by the present invention can generate bearing fault data under stable working conditions using only a small amount of bearing fault data under unstable working conditions. The method only needs to train a denoising diffusion model that can generate stable working condition data to generate bearing fault data under specified working conditions. The generated data of the specified working condition has the same data distribution characteristics as the existing stable working condition data, which solves the problem of low bearing fault diagnosis accuracy caused by inconsistent distribution of domain generalized bearing fault diagnosis data, improves the accuracy of bearing fault diagnosis, and is more suitable for bearing fault diagnosis in different bearing application scenarios.

[0033] This method is not limited to the fault diagnosis of bearings in high-speed train bogies, but is also applicable to intelligent fault diagnosis of rotating machinery in other fields. Of course, it is not limited to the fault diagnosis of rotating machinery bearings. Gearboxes and planetary gears in rotating machinery also involve intelligent fault diagnosis, and this method is also applicable to intelligent fault diagnosis of such rotating machinery.

[0034] Other beneficial effects of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of the steps of the present invention;

[0036] Figure 2 Schematic diagram of the process of performing the Repaint method on a two-dimensional time-spectrum image in an embodiment of the present invention, where (a) is a two-dimensional time-spectrum image under time-varying conditions, (b) is a two-dimensional time-spectrum image with a 7 / 8 mask, and (c) is a two-dimensional time-spectrum image under stable conditions after the Repaint method.

[0037] Figure 3 Schematic diagram of a high-speed train bearing experimental device in an embodiment of the present invention;

[0038] Figure 4 Schematic diagram of the results of bearing fault diagnosis accuracy in an embodiment of the present invention; DETAILED DESCRIPTION

[0039] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0040] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0041] It should also be noted that the diagrams provided in the following embodiments are merely schematic illustrations of the basic concepts of the present disclosure. The diagrams only show components relevant to the present disclosure and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the configuration, quantity, and proportion of each component may be varied at will, and the component layout may be more complex. Furthermore, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will appreciate that the described aspects may be practiced without these specific details.

[0042] like Figure 1 As shown, an embodiment of the present invention provides a bearing fault diagnosis method based on time-varying working condition transfer learning of a diffusion model, which specifically includes the following steps:

[0043] S1, obtain the original fault vibration signal of the bearing and perform preprocessing.

[0044] In this step, for high-speed train bearing fault diagnosis, the bearing fault vibration signal of the high-speed train is collected, and the one-dimensional vibration signal is converted into a two-dimensional time-frequency spectrum image using time-frequency analysis of complex Morlet wavelet transform.

[0045] It should be noted that the bandwidth of the complex Morlet wavelet transform used in this embodiment is 3, the center frequency is 3, and the total scale is 256. The bearing fault vibration signal in this embodiment includes fault data of four types of high-speed train actual vehicle bearings.

[0046] S2, constructs a denoising diffusion model that can generate two-dimensional time-frequency spectrum images of stable working conditions.

[0047] In this step, the acquired two-dimensional time-frequency spectrum image of the stable working condition is put into the denoising diffusion model for model training, so that the denoising diffusion model can learn the image features of the two-dimensional time-frequency spectrum image of the stable working condition.

[0048] Specifically in this embodiment, S2 can be decomposed into the following sub-steps:

[0049] S21, the two-dimensional time-frequency spectrum image x0 of the stable working condition is input into the denoising diffusion model. At each step t (t = 1, 2, ..., T), the denoising diffusion model adds Gaussian noise to it using the following formula to transform it into a Gaussian white noise image with independent distributions:

[0050]

[0051] Among them, t represents the step size, that is, the current number of iterations; α t represents the adjustment parameter of the tth step, and its size decreases as the step length increases; ε t It follows a standard normal distribution The sampling noise.

[0052] S22, gradually denoise the Gaussian white noise image of each independent distribution and restore it to a two-dimensional time-frequency spectrum image similar to the original image, that is, for the Gaussian pure noise data sample x T Initially, at each step t (t = T, T-1, ..., 1) step-by-step noise removal is performed as shown below:

[0053]

[0054] Among them, ε θ (x t ,t) is the prediction noise learned by S21; σ t It is to adjust the parameters to prevent the model from falling into the local optimum.

[0055] S23, repeating S21 to S22 multiple times to obtain a denoising diffusion model that can generate a two-dimensional time-frequency spectrum image of a stable working condition, and saving the trained denoising diffusion model.

[0056] S3, through the Repaint method of two-dimensional time-spectrum image, a complete bearing fault dataset under stable working conditions is constructed based on the vibration signals of time-varying working conditions.

[0057] In this step, the vibration signal of the time-varying working condition (unstable working condition) is masked according to the established masking rules. The Repaint method generates a continuous stable vibration signal that conforms to the vibration signal law from the masked area based on the unmasked part of the vibration signal of the unstable working condition and the image features learned by the denoising diffusion model, thereby constructing a complete bearing fault dataset under stable working conditions.

[0058] The purpose of this step is to make the two-dimensional time-frequency spectrum image maintain the characteristics of the original small part of the speed, and at the same time generate the data of the current stable working condition based on the existing denoising diffusion model, which ultimately solves the problem of data scarcity under time-varying working conditions in the domain generalization process.

[0059] It should be noted that the Repaint method is a technology for data generation and repair, primarily used in signal processing and computer vision. It is primarily used to restore a complete image from a partially missing or damaged image, or to generate a continuous signal that conforms to specific patterns in signal processing. In this embodiment, by inputting a partially masked time-varying signal into a denoising diffusion model and utilizing the image features of the stable signal learned by the model to generate a complete stable signal, the robustness of signal processing can be effectively improved, helping to generate a high-quality dataset for further analysis and diagnosis.

[0060] At the same time Figure 2 As shown, specifically in this embodiment, S3 can be decomposed into the following sub-steps:

[0061] S31 , masking the two-dimensional time-frequency spectrum image of the time-varying working condition longitudinally. As a preferred embodiment, the left 1 / 8 position of the two-dimensional time-frequency spectrum image of the time-varying working condition is retained, and the right side is masked with a black image.

[0062] S32, generates a stable two-dimensional time-frequency spectrum image of the masked area through the denoising diffusion model;

[0063] S33, repeating S32 multiple times, and finally generating an image including a complete and stable two-dimensional time-frequency spectrum image in which the image of the Repaint area and the original image are perfectly integrated.

[0064] S4, performs generalized bearing fault diagnosis in the field of time-varying working conditions.

[0065] In this step, bearing fault diagnosis is performed using the S3-expanded bearing fault dataset and domain generalization methods. The specific steps are as follows:

[0066] S41, taking the bearing fault data of the stable working condition at low speed and the generated bearing fault data of the stable working condition as the source domain, and the bearing fault data of the stable working condition at high speed as the target domain of domain generalization.

[0067] S42, using the domain generalization method to train the model based on the source domain data, perform bearing fault diagnosis on the target domain data, output the accuracy of the bearing fault diagnosis, and determine the bearing fault condition.

[0068] In this embodiment, the domain generalization methods include MMD, Mixup, ERM, DANN and ANDMask methods. Maximum Mean Discrepancy (MMD) is a distance metric used for domain alignment, which measures the distribution difference between the source domain and the target domain data, and the goal is to minimize the distribution difference between the source domain and the target domain. Mixup is a data augmentation method that enhances the robustness of the model by generating new training samples by linearly interpolating samples during training. Empirical Risk Minimization (ERM) is a classic training strategy that aims to train the model by minimizing the loss function on the training set. Domain-Adversarial Neural Network (DANN) reduces the difference between the source domain and the target domain through adversarial training. Adaptive Network Masking (ANDMask) aims to align the source domain and target domain data by adaptively adjusting the feature space.

[0069] Therefore, this method only uses a small amount of bearing fault data under unstable working conditions to generate bearing fault data under stable working conditions; this method only needs to train a denoising diffusion model that can generate stable working condition data to generate bearing fault data under specified working conditions, and the generated data under specified working conditions has the same data distribution characteristics as the existing stable working condition data, which solves the problem of low bearing fault diagnosis accuracy caused by inconsistent data distribution in domain generalization bearing fault diagnosis.

[0070] It should be noted that this method is not limited to the bearing fault diagnosis of high-speed train bogies, but is also applicable to the intelligent fault diagnosis of rotating machinery in other fields. Of course, it is not limited to the fault diagnosis of rotating machinery bearings. Gearboxes, planetary gears, etc. in rotating machinery all involve intelligent fault diagnosis, and this method is also applicable to the intelligent fault diagnosis of this type of rotating machinery.

[0071] The following example further illustrates the effectiveness of this method. Unrepainted 2D time-frequency spectrum data is selected as a reference for domain generalization to verify the effectiveness of bearing fault diagnosis after repainting. To ensure data consistency, the unrepainted variable speed data is sliced ​​so that the next 2D time-frequency spectrum image retains some data from the previous 2D time-frequency spectrum image. The repainted variable speed data is sliced ​​so that adjacent 2D time-frequency spectrum images do not have duplicate vibration information.

[0072] This case uses real high-speed train bogie bearing failure data for verification. NTNCRI-2692 double-row tapered roller bearings for high-speed trains are used as the research object. Test pieces with normal, indented, spalled, and corroded characteristics are selected and installed on two sets of bogies in the entire rolling test bench. Figure 3 As shown, the left and right sides of the bogie are divided into left and right bearings. The four axles of the two bogies are defined as the first, second, third, and fourth axles, from front to back. Different types of bearing faults were installed in the train for data collection. The test subjects covered eight bearings, with two measurement points on each bearing, for a total of 16 sensors. The test speed ranges were 100 km / h, 100-150 km / h, and 150 km / h, with a sampling time of 20 seconds and a sampling frequency of 25.6 kHz.

[0073] Based on the collected bearing fault data, this method is used to diagnose bearing faults. First, the one-dimensional data of the high-speed train bogie bearing fault is converted into a two-dimensional time-frequency spectrum image. Then, the two-dimensional time-frequency spectrum images of the bearing fault at speeds of 100km / h and 150km / h are used to train the denoising diffusion model. The two-dimensional time-frequency spectrum images of the bearing fault at 100-150km / h are masked by 7 / 8, and the trained denoising diffusion model is used to generate images of the mask positions. Finally, the two-dimensional time-frequency spectrum images at 100km / h and the generated 100-150km / h are used as the source domain, and the two-dimensional time-frequency spectrum images at 150km / h are used as the target domain. The domain generalization method is used to diagnose bearing faults, and the accuracy of the bearing fault diagnosis is output. The higher the accuracy of the bearing fault diagnosis, the more accurate the diagnosis. Figure 4 The comparison results are shown in Table 1.

[0074] Table 1 Test results of domain generalized bearing fault diagnosis before and after Repaint

[0075] Domain Generalization Method MMD Mixup ERM DANN ANDmask No Repaint 87.38 92.19 78.54 82.18 83.03 Repaint 97.23 97.4 98.13 96.73 92.55

[0076] Depend on Figure 4As can be seen from Table 1, the bearing fault diagnosis accuracy after Repaint is significantly higher than that without Repaint. This further proves that the method provided in this embodiment can solve the problem of low bearing fault diagnosis accuracy caused by inconsistent distribution of domain-generalized bearing fault diagnosis data, and improve the accuracy of bearing fault diagnosis.

[0077] Based on the same inventive concept, this embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the aforementioned time-varying working condition transfer learning bearing fault diagnosis method based on a diffusion model.

[0078] Computer-readable media include, but are not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROMs, RAMs, EPROMs (Erasable Programmable Read-Only Memory), EEPROMs, flash memories, magnetic cards, or optical cards. In other words, computer-readable media include any medium that can store or transmit information in a form that can be read by a device (e.g., a computer).

[0079] Based on the same inventive concept, this embodiment further provides an electronic device comprising at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the aforementioned method for bearing fault diagnosis using time-varying operating condition transfer learning based on a diffusion model.

[0080] The computer-readable storage medium, electronic device, etc. provided in this embodiment have the same inventive concept and the same beneficial effects as the aforementioned method, and are not described in detail here.

[0081] The technical features of the above embodiments can be combined arbitrarily. In order 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.

[0082] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A bearing fault diagnosis method based on time-varying working condition transfer learning of diffusion model, characterized by: The steps include: S1, obtain the original fault vibration signal of the bearing and preprocess it to obtain a two-dimensional time-frequency spectrum image; S2, constructing a denoising diffusion model, placing the acquired two-dimensional time-frequency spectrum image of the stable working condition into the denoising diffusion model for model training, so that the denoising diffusion model learns the image features of the two-dimensional time-frequency spectrum image of the stable working condition; S3: Using the Repaint method of a two-dimensional time-spectrum image, the vibration signal of the time-varying working condition is masked according to the established masking rules. Based on the unmasked portion of the vibration signal of the unstable working condition and the image features learned by the denoising diffusion model, the Repaint method generates a continuous stable vibration signal that conforms to the vibration signal law from the masked area, thus constructing a complete bearing fault dataset under stable working conditions. S4, uses the bearing fault dataset expanded by S3 and the domain generalization method to perform bearing fault diagnosis.

2. The method for bearing fault diagnosis based on time-varying working condition transfer learning of diffusion model according to claim 1 is characterized in that: In S1, the vibration signal of the bearing fault is collected and the one-dimensional vibration signal is converted into a two-dimensional time-frequency spectrum image using time-frequency analysis of complex morlet wavelet transform.

3. The bearing fault diagnosis method based on time-varying working condition transfer learning of diffusion model according to claim 1 is characterized in that: S2 includes the following sub-steps: S21, the two-dimensional time-frequency spectrum image x0 of the stable working condition is input into the denoising diffusion model. At each step t, the denoising diffusion model adds Gaussian noise to the two-dimensional time-frequency spectrum image using the following formula, so that the two-dimensional time-frequency spectrum image becomes a Gaussian white noise image with independent distributions: Where t represents the step size, t=1,2,...,T; α t represents the adjustment parameter of the tth step, which decreases as the step length increases; ε t It follows a standard normal distribution Sampling noise; S22, gradually denoise the independently distributed Gaussian white noise image and restore it to a two-dimensional time-frequency spectrum image similar to the original image. T Initially, step-by-step noise removal is performed at each step t, as shown below: Among them, ε θ (x t ,t) is the prediction noise learned by S21; σ t is the adjustment parameter; S23, repeating S21 to S22 multiple times to obtain a denoising diffusion model capable of generating a two-dimensional time-frequency spectrum image of a stable working condition, and saving the denoising diffusion model.

4. The method for bearing fault diagnosis based on time-varying working condition transfer learning of diffusion model according to claim 1 is characterized in that: S3 includes the following sub-steps: S31, longitudinally masking the two-dimensional time-frequency spectrum image of the time-varying working condition; S32, generating a stable two-dimensional time-frequency spectrum image of the masked area by the Repaint method; S33, repeating S32 multiple times until the generated image includes a complete two-dimensional time-frequency spectrum image of a stable working condition, in which the image of the Repaint area is fused with the original image.

5. The method for bearing fault diagnosis based on time-varying working condition transfer learning of diffusion model according to claim 4 is characterized in that: In S31, the left 1 / 8 position of the two-dimensional time-frequency spectrum image of the time-varying working condition is retained, and the right side is covered with a black image.

6. The method for bearing fault diagnosis based on time-varying working condition transfer learning of diffusion model according to claim 1 is characterized in that: S4 includes the following sub-steps: S41, using the bearing fault data of the stable working condition at low speed and the generated bearing fault data of the stable working condition as the source domain, and the bearing fault data of the stable working condition at high speed as the target domain of domain generalization; S42, using the domain generalization method to train the model based on the source domain data, perform bearing fault diagnosis on the target domain data, output the accuracy of the bearing fault diagnosis, and determine the bearing fault condition.

7. The method for bearing fault diagnosis based on time-varying working condition transfer learning of diffusion model according to claim 6 is characterized in that: Domain generalization methods include MMD, Mixup, ERM, DANN and ANDMask methods.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a bearing fault diagnosis method based on time-varying working condition transfer learning of a diffusion model is implemented as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: It includes at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a bearing fault diagnosis method based on time-varying working condition transfer learning of a diffusion model as described in any one of claims 1 to 7.

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