Vibration fault diagnosis method, device and system for nuclear power steam turbine, and storage medium

Abstract

The invention discloses a nuclear power steam turbine vibration fault diagnosis method, device and system, and a storage medium. The method comprises the following steps: S1, obtaining historical operation data of a nuclear power steam turbine in a nuclear power station; s2, according to historical operation data of the nuclear power turbine in the nuclear power station, obtaining a fault diagnosis model considering privacy protection and based on federal depth domain adaptation; and S3, inputting the real-time nuclear power station operation data into the fault diagnosis model considering privacy protection and based on federal depth domain adaptation to carry out nuclear power turbine vibration fault diagnosis. By adopting the technical scheme of the invention, the fault category can be quickly identified under the consideration of privacy protection, so that maintenance personnel and a system can quickly respond to the fault.

Description

Technical Field

[0001] The present invention belongs to the technical field of nuclear power plant fault diagnosis, and in particular to a method and device, system, and storage medium for diagnosing vibration faults of nuclear power steam turbines. Background Art

[0002] With the increasing awareness of environmental protection, sustainable manufacturing has become an important part of various industries. The safe and reliable operation and maintenance of nuclear power steam turbines are crucial, which requires agile and accurate response and diagnosis of equipment fault signals. Due to safety requirements, nuclear power plants strictly isolate operating data, forming actual data islands. At the same time, the insufficient amount of fault sample data makes it difficult to establish accurate fault diagnosis. How to establish stable and reliable nuclear power steam turbine vibration fault diagnosis between different nuclear power plants and nuclear equipment is an urgent problem to be solved. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a nuclear power steam turbine vibration fault diagnosis method and device, system, and storage medium, which can quickly identify the fault category while considering privacy protection, so that maintenance personnel and the system can respond quickly to the fault.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] A method for diagnosing vibration faults of a nuclear power steam turbine, comprising:

[0006] Step S1, obtaining historical operation data of a nuclear power steam turbine in a nuclear power plant;

[0007] Step S2: obtaining a fault diagnosis model based on federated deep domain adaptation and considering privacy protection according to historical operation data of nuclear power steam turbines in nuclear power plants;

[0008] Step S3: input the real-time nuclear power plant operation data into the fault diagnosis model based on federated deep domain adaptation taking privacy protection into consideration to perform nuclear power steam turbine vibration fault diagnosis.

[0009] Preferably, the nuclear power plant includes: a source nuclear power plant and a target nuclear power plant, wherein the source nuclear power plant includes multiple source nuclear-powered steam turbines and the target nuclear power plant includes multiple target nuclear-powered steam turbines; when the nuclear power plant initiates a request with a preset type of nuclear-powered steam turbine as the target, the central server will select the source nuclear-powered steam turbine from its subordinate nuclear power plants.

[0010] Preferably, step S2 comprises:

[0011] According to the historical operation data of nuclear power steam turbines in nuclear power plants, the deep temporal features are extracted from the fault diagnosis signals of nuclear power steam turbines using the feature extractor based on BiLSTM-CNN to obtain the equipment features of the source nuclear power steam turbine and the target nuclear power steam turbine.

[0012] According to the equipment characteristics of the source nuclear-powered steam turbine and the target nuclear-powered steam turbine, a fault diagnosis model is obtained through local knowledge transfer of domain adversarial training and an adaptive weight mechanism for dynamic federated learning.

[0013] The present invention also provides a nuclear power steam turbine vibration fault diagnosis device, comprising:

[0014] An acquisition module is used to acquire historical operation data of nuclear power steam turbines in nuclear power plants;

[0015] A training module is used to obtain a fault diagnosis model based on federated deep domain adaptation with privacy protection in mind based on historical operating data of nuclear power steam turbines in nuclear power plants;

[0016] The fault diagnosis module is used to input real-time nuclear power plant operation data into a fault diagnosis model based on federated deep domain adaptation with privacy protection to perform nuclear power steam turbine vibration fault diagnosis.

[0017] Preferably, the nuclear power plant includes: a source nuclear power plant and a target nuclear power plant, wherein the source nuclear power plant includes multiple source nuclear-powered steam turbines and the target nuclear power plant includes multiple target nuclear-powered steam turbines; when the nuclear power plant initiates a request with a preset type of nuclear-powered steam turbine as the target, the central server will select the source nuclear-powered steam turbine from its subordinate nuclear power plants.

[0018] Preferably, the training device comprises:

[0019] An extraction unit is used to extract deep time features from the fault diagnosis signal of the nuclear power steam turbine according to the historical operation data of the nuclear power steam turbine in the nuclear power plant by using a feature extractor based on BiLSTM-CNN to obtain the equipment features of the source nuclear power steam turbine and the target nuclear power steam turbine;

[0020] The training unit is used to obtain a fault diagnosis model according to the equipment characteristics of the source nuclear-powered steam turbine and the target nuclear-powered steam turbine through local knowledge transfer of domain adversarial training and an adaptive weight mechanism for dynamic federated learning.

[0021] An embodiment of the present invention also provides a nuclear power steam turbine vibration fault diagnosis system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a nuclear power steam turbine vibration fault diagnosis method when executed by the processor.

[0022] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program executes a method for diagnosing vibration faults of a nuclear power steam turbine when the computer program is run.

[0023] The present invention adopts a fault diagnosis model based on federated deep domain adaptation with consideration of privacy protection, and performs feature extraction locally in the source nuclear power plant and the target nuclear power plant, so that features can be safely shared without leaking data privacy; at the same time, domain adversarial training is integrated into local model training to achieve the transfer of vibration fault diagnosis knowledge; in addition, an adaptive weight mechanism is designed to promote the adaptive adjustment of model weights in the federated aggregation process. The technical solution of the present invention can quickly identify fault categories while considering privacy protection, so that maintenance personnel and systems can respond quickly to faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0025] Figure 1 This is a flow chart of a method for diagnosing vibration faults of a nuclear power steam turbine according to an embodiment of the present invention;

[0026] Figure 2 The schematic diagram of the vibration fault diagnosis method of a nuclear power steam turbine according to an embodiment of the present invention;

[0027] Figure 3 Schematic diagram of the dynamic adversarial adaptive module DAAM. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] Embodiment 1:

[0031] like Figure 1 , 2 As shown, an embodiment of the present invention provides a method for diagnosing vibration faults of a nuclear power steam turbine, comprising:

[0032] Step S1, obtaining historical operation data of a nuclear power steam turbine in a nuclear power plant;

[0033] Step S2: obtaining a fault diagnosis model based on federated deep domain adaptation and considering privacy protection according to historical operation data of nuclear power steam turbines in nuclear power plants;

[0034] Step S3: input the real-time nuclear power plant operation data into the fault diagnosis model based on federated deep domain adaptation taking privacy protection into consideration to perform nuclear power steam turbine vibration fault diagnosis.

[0035] As an implementation method of an embodiment of the present invention, a nuclear power plant includes: a source nuclear power plant SNPP and a target nuclear power plant TNPP, wherein the source nuclear power plant includes multiple source nuclear power steam turbines SST, and the target nuclear power plant includes multiple target nuclear power steam turbines TST; when the nuclear power plant initiates a request with a preset type of nuclear power steam turbine as the target, the central server will select the source nuclear power steam turbine from its subordinate nuclear power plants. The nuclear power steam turbines in each SNPP are similar devices operating under different operating conditions, and the data sets of these devices have the same label space and different marginal distributions.

[0036] As an implementation of the embodiment of the present invention, step S2 includes:

[0037] Step S21, according to the historical operation data of the nuclear power steam turbine in the nuclear power plant, using the feature extractor based on BiLSTM-CNN to extract the deep time features from the fault diagnosis signal of the nuclear power steam turbine, and obtain the equipment features of the source nuclear power steam turbine and the target nuclear power steam turbine;

[0038] Step S22: According to the equipment characteristics of the source nuclear-powered steam turbine and the target nuclear-powered steam turbine, a fault diagnosis model is obtained through local knowledge transfer of domain adversarial training and an adaptive weight mechanism for dynamic federated learning.

[0039] Furthermore, in step S21, the feature extractor based on BiLSTM-CNN consists of 2 BiLSTM layers, 1 LSTM layer and 2 convolutional layers. Batch normalization (BN) is established after the convolutional layer to accelerate the convergence of feature extraction. In addition, a rectified linear unit (ReLU) activation function is designed after the BN layer, which together with BiLSTM can alleviate the gradient vanishing and gradient exploding problems.

[0040] For the SST data sample of the i-th SNPP Using a parameter θ f，i The feature extractor G f，i To obtain the features in the source domain Right now:

[0041]

[0042] Accordingly, the features in the target domain are obtained in TST. The only transmission between SNPP and TNPP is the vibration fault feature of nuclear power steam turbine.

[0043] Furthermore, in step S21, when a new device is put into use, it will not generate enough labeled data sets during its short-term operation, and will face the problem of high cost and long consumption of labeling these data. In order to bridge the gap between historical equipment operation data and new equipment operation data, a dynamic adversarial adaptation method is introduced to learn transferable features in nuclear power steam turbine vibration fault diagnosis, and a DAAM module (dynamic adversarial adaptive module) for FL (federated learning) is proposed, as shown in Figure 2. Figure 3 As shown in Figure 2, the DAAM module is based on the idea of ​​Domain Adversarial Network (DAN) and achieves local knowledge transfer by leveraging domain adversarial training.

[0044] Furthermore, each SNPP will use the local SSTs dataset to train the DAAM module. The DAAM module consists of a label classifier G y,i , global domain discriminator G d,i and local subdomain discriminator When performing local knowledge transfer, it is necessary to consider not only the difference in domain edge distribution between vibration fault categories (subdomains), but also the difference in conditional distribution. The training process of the SSTs dataset of the i-th SNPP is as follows:

[0045] In the label classifier, the source features extracted by the feature extractor are used and its corresponding tags As input, the label classifier is trained. The loss function of the label classifier is the cross entropy loss, which is defined as follows:

[0046]

[0047] Among them, G y,i and θ y,j Represent the label classifier and its parameters composed of the fully connected layer, express The corresponding label.

[0048] The loss function calculation formula of the global domain discriminator is as follows:

[0049]

[0050] Among them, G d,i and θ d,i are the global domain discriminator and its parameters, f i,j Depend on and Composition, p j Represents input The domain label (if f i,j From Then p j =0; if from Then p j =1).

[0051] In order to achieve more fine-grained domain adaptation in the vibration fault knowledge domain, a local subdomain discriminator is used to align the conditional distribution. For the vibration fault problem with n categories, the loss function of the local subdomain discriminator is defined as follows:

[0052]

[0053] in, and Respectively represent the rth local domain discriminator and its parameters, Yes i,j The corresponding predicted probability distribution.

[0054] In summary, the total loss function of the DAAM module consists of the label classifier loss, the global domain discriminator loss, and the local subdomain discriminator loss, and its expression is as follows:

[0055] Loss i =L y,i -β((1-ω i )L g,i +ω i L l,i )

[0056] Among them, β is a trade-off parameter used to adjust Loss i The global domain discriminator loss L g,i and the local subdomain discriminator loss L l,i The weight, ω i is a dynamic adversarial factor that is used to assess the importance between the marginal distribution and the conditional distribution.

[0057] According to the SSTs dataset of i SNPP TST dataset with TNPP The marginal distribution difference and conditional distribution difference between them, the dynamic adversarial factor ω i In the iterative learning process, the weights of the global domain discriminator loss and the local subdomain discriminator loss in the domain loss will be automatically adjusted. The estimation formula is as follows:

[0058]

[0059] in, and Respectively and The rth category, and They are the A distances of the global domain discriminator and the local subdomain discriminator, respectively, and the calculation formula is as follows:

[0060]

[0061] After the optimization target is determined, the total loss function of DAAM can be updated as:

[0062]

[0063] Among them, Θ i Represents all the parameters that DAAM needs to train, represented by θ y,i ,θ d,i , composition.

[0064] Further, in step S22, in the nuclear power steam turbine vibration fault diagnosis, the data sets owned by each SNPP have significant differences in quality and quantity. On the one hand, the classifiers trained based on the SST data set have different performances; on the other hand, there are marginal distribution differences and conditional distribution differences between the SST (source domain) and TST data sets (target domain). The present invention designs an adaptive weight mechanism for dynamic federated learning. The central server comprehensively considers the classification performance and domain adaptability in the SNPP, and dynamically adjusts the weight of each SNPP in the global model.

[0065] Furthermore, in the vibration fault diagnosis of nuclear power units, the datasets of various SNPPs have significant differences in quality and quantity. On the one hand, there are differences in the performance of classifiers trained based on SSTs datasets; on the other hand, there are differences in marginal distribution and conditional distribution between SSTs (source domain) and TST (target domain) datasets. In response to the problem studied in this paper, we designed an adaptive weight mechanism for dynamic federated learning. The central server can dynamically adjust the weight of each SNPP in the global model by comprehensively considering the classification performance of the model and the domain adaptation ability in the SNPP.

[0066] In the process of local knowledge transfer, the DAAM loss of the local model is i Including the classifier loss L y,i , global domain discriminator loss L g,i and the local subdomain discriminator loss L l,i The classifier loss L y,i It can measure the classification performance of the model, while the global domain discriminator loss L g,i and the local subdomain discriminator loss L l,iThe domain adaptation ability of the model can be evaluated. Therefore, the designed weight adaptation mechanism uses the local model loss Loss i Calculate the ratio of the local model to the global model.

[0067] Through dynamic federated learning, the central server extracts N from all SNPPs according to the extraction ratio Frac SC source clients participate in global training. g The local model loss of the i-th SNPP is Loss i , the similarity between the local model of the i-th SNPP and the TST target model is defined as:

[0068]

[0069] Therefore, the similarity ζ i The weights in the global model are expressed as:

[0070]

[0071] Among them, N SC is the number of SNPPs selected according to the extraction ratio Frac.

[0072] In the i g After global training, the model of each SNPP and similarity The data will be transmitted to the central server. The central server calculates the weight of each model in the global model based on the obtained similarity and aggregates the models through an adaptive weight mechanism. The calculation formula is as follows:

[0073]

[0074] in, Represents the local knowledge obtained after transfer

[0075] Embodiment 2:

[0076] The embodiment of the present invention further provides a nuclear power steam turbine vibration fault diagnosis device, comprising:

[0077] An acquisition module is used to acquire historical operation data of nuclear power steam turbines in nuclear power plants;

[0078] A training module is used to obtain a fault diagnosis model based on federated deep domain adaptation with privacy protection in mind based on historical operating data of nuclear power steam turbines in nuclear power plants;

[0079] The fault diagnosis module is used to input real-time nuclear power plant operation data into a fault diagnosis model based on federated deep domain adaptation with privacy protection to perform nuclear power steam turbine vibration fault diagnosis.

[0080] As an implementation mode of an embodiment of the present invention, a nuclear power plant includes: a source nuclear power plant and a target nuclear power plant, wherein the source nuclear power plant includes multiple source nuclear-powered steam turbines, and the target nuclear power plant includes multiple target nuclear-powered steam turbines; when the nuclear power plant initiates a request with a preset type of nuclear-powered steam turbine as the target, the central server will select the source nuclear-powered steam turbine from its subordinate nuclear power plants.

[0081] As an implementation of an embodiment of the present invention, the training device includes:

[0082] An extraction unit is used to extract deep time features from the fault diagnosis signal of the nuclear power steam turbine according to the historical operation data of the nuclear power steam turbine in the nuclear power plant by using a feature extractor based on BiLSTM-CNN to obtain the equipment features of the source nuclear power steam turbine and the target nuclear power steam turbine;

[0083] The training unit is used to obtain a fault diagnosis model according to the equipment characteristics of the source nuclear-powered steam turbine and the target nuclear-powered steam turbine through local knowledge transfer of domain adversarial training and an adaptive weight mechanism for dynamic federated learning.

[0084] Embodiment 3:

[0085] An embodiment of the present invention also provides a nuclear power steam turbine vibration fault diagnosis system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a nuclear power steam turbine vibration fault diagnosis method when executed by the processor.

[0086] Embodiment 4:

[0087] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program executes a method for diagnosing vibration faults of a nuclear power steam turbine when the computer program is run.

[0088] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for diagnosing vibration faults of a nuclear power steam turbine, characterized in that: include: Step S1, obtaining historical operation data of a nuclear power steam turbine in a nuclear power plant; Step S2: obtaining a fault diagnosis model based on federated deep domain adaptation and considering privacy protection according to historical operation data of nuclear power steam turbines in nuclear power plants; Step S3: input the real-time nuclear power plant operation data into the fault diagnosis model based on federated deep domain adaptation taking privacy protection into consideration to perform nuclear power steam turbine vibration fault diagnosis.

2. The nuclear power steam turbine vibration fault diagnosis method according to claim 1, characterized in that: The nuclear power plant includes: a source nuclear power plant and a target nuclear power plant, wherein the source nuclear power plant includes multiple source nuclear-powered steam turbines, and the target nuclear power plant includes multiple target nuclear-powered steam turbines; when the nuclear power plant initiates a request with a preset type of nuclear-powered steam turbine as the target, the central server will select the source nuclear-powered steam turbine from its subordinate nuclear power plants.

3. The method for diagnosing vibration faults of a nuclear power steam turbine according to claim 2, characterized in that: Step S2 includes: According to the historical operation data of nuclear power steam turbines in nuclear power plants, the deep temporal features are extracted from the fault diagnosis signals of nuclear power steam turbines using the feature extractor based on BiLSTM-CNN to obtain the equipment features of the source nuclear power steam turbine and the target nuclear power steam turbine. According to the equipment characteristics of the source nuclear-powered steam turbine and the target nuclear-powered steam turbine, a fault diagnosis model is obtained through local knowledge transfer of domain adversarial training and an adaptive weight mechanism for dynamic federated learning.

4. A nuclear power steam turbine vibration fault diagnosis device, characterized in that: include: An acquisition module is used to acquire historical operation data of nuclear power steam turbines in nuclear power plants; A training module is used to obtain a fault diagnosis model based on federated deep domain adaptation with privacy protection in mind based on historical operating data of nuclear power steam turbines in nuclear power plants; The fault diagnosis module is used to input real-time nuclear power plant operation data into a fault diagnosis model based on federated deep domain adaptation with privacy protection to perform nuclear power steam turbine vibration fault diagnosis.

5. The nuclear power steam turbine vibration fault diagnosis device according to claim 4, characterized in that: The nuclear power plant includes: a source nuclear power plant and a target nuclear power plant, wherein the source nuclear power plant includes multiple source nuclear-powered steam turbines, and the target nuclear power plant includes multiple target nuclear-powered steam turbines; when the nuclear power plant initiates a request with a preset type of nuclear-powered steam turbine as the target, the central server will select the source nuclear-powered steam turbine from its subordinate nuclear power plants.

6. The nuclear power steam turbine vibration fault diagnosis device according to claim 5, characterized in that: The training device includes: An extraction unit is used to extract deep time features from the fault diagnosis signal of the nuclear power steam turbine according to the historical operation data of the nuclear power steam turbine in the nuclear power plant by using a feature extractor based on BiLSTM-CNN to obtain the equipment features of the source nuclear power steam turbine and the target nuclear power steam turbine; The training unit is used to obtain a fault diagnosis model according to the equipment characteristics of the source nuclear-powered steam turbine and the target nuclear-powered steam turbine through local knowledge transfer of domain adversarial training and an adaptive weight mechanism for dynamic federated learning.

7. A nuclear power steam turbine vibration fault diagnosis system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the nuclear power steam turbine vibration fault diagnosis method as described in any one of claims 1 to 3 is executed.

8. A storage medium, characterized in that: The storage medium stores a computer program, which executes the nuclear power steam turbine vibration fault diagnosis method according to any one of claims 1 to 3 when running.

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