Vacuum pump fault diagnosis method for fusion reactor strong electromagnetic environment

Abstract

The invention relates to the technical field of vacuum pump fault diagnosis, in particular to a fusion reactor strong electromagnetic environment-oriented vacuum pump fault diagnosis method. According to the technical scheme, the method comprises the following steps that a vacuum pump fault test bed with the adjustable electromagnetic field intensity is built, vacuum pump vibration acceleration signals, three-phase current signals and electromagnetic field intensity signals under different electromagnetic field intensities are collected through the test bed, and a simulation environment fault data set is formed; during the operation period of the fusion reactor, the operation data and electromagnetic interference spectrum of the vacuum pump are collected in real time. By simulating the strong electromagnetic environment of the fusion reactor and combining real operation data, the problem of failure of a traditional vacuum pump fault diagnosis method under electromagnetic interference is innovatively solved, the adaptability and reliability of a diagnosis model are remarkably improved, the limitation of small sample data is effectively overcome by using the data enhancement and cross-domain joint training technology, and the method is suitable for large-scale popularization and application. Accurate identification of various fault types is realized, and an important technical guarantee is provided for safe and stable operation of the fusion device.

Description

Technical Field

[0001] The present invention relates to the technical field of vacuum pump fault diagnosis, and particularly to a vacuum pump fault diagnosis method for a strong electromagnetic environment of a fusion reactor. Background Art

[0002] The vacuum pump group is one of the auxiliary key devices of a fusion device. Whether it is the EAST (Experimental Advanced Superconducting Tokamak) fusion device in China, or the largest fusion device under construction in the world, the ITER (International Thermonuclear Experimental Reactor), including the future CFETR (China Fusion Engineering Test Reactor) fusion device in China, a large vacuum pump group is required to provide a good vacuum environment for the fusion device. The total operating power of the fusion device and system usually exceeds 30 MW, and each pulse experiment lasts for 10 - 1000 s, or even longer. As a result, an extremely complex and harsh electromagnetic field environment will be generated. The electromagnetic field in the fusion reactor is mainly generated by the interaction of strong currents and magnetic fields. These environmental factors not only affect the stability of the fusion reaction, but also pose a threat to the safety of the equipment. Many measurement and control devices are around or even inside the fusion device, very close to the electromagnetic interference sources, and are extremely vulnerable to electromagnetic interference, such as many diagnostic devices, solenoid valves for controlling valves, probes for electromagnetic measurement, and equipment of the quench protection system, etc. Among them, the influence of electromagnetic interference on the vacuum pump is significant, which may cause problems such as abnormal solenoid valves of control components, degradation of equipment performance, or shutdown of the equipment. In such a complex electromagnetic environment, the vacuum pump group, as the core device for maintaining the internal vacuum state of the fusion reactor, plays a crucial role. The performance of the vacuum pump group is directly related to the stability and reaction efficiency of the plasma. Therefore, the monitoring of its operating state and fault diagnosis are particularly important. Therefore, when using a vacuum pump, the influence of electromagnetic interference needs to be fully considered, and corresponding measures should be taken to reduce the influence of interference on the equipment and system. The GTEM cell is a test system designed based on the principle of coaxial and asymmetric matrix transmission lines, which can generate a relatively large radio frequency electromagnetic field intensity and is suitable for simulating the strong electromagnetic interference environment of a fusion reactor.

[0003] Therefore, this application proposes a vacuum pump fault diagnosis method for a strong electromagnetic environment of a fusion reactor. Summary of the Invention

[0004] The object of the present invention is to propose a vacuum pump fault diagnosis method for a strong electromagnetic environment of a fusion reactor to solve the problem that there is no monitoring and fault diagnosis method for the operating state of the vacuum pump group in the background art.

[0005] Technical solution of the present invention: A method for diagnosing vacuum pump faults in a strong electromagnetic environment for a fusion reactor, comprising the following steps: (1) Build a vacuum pump fault test bench with adjustable electromagnetic field strength, and collect the vibration acceleration signal, three-phase current signal and electromagnetic field strength signal of the vacuum pump under different electromagnetic field strengths through the test bench to form a simulation environment fault data set; (2) During the operation of the fusion reactor, collect the operation data and electromagnetic interference spectrum of the vacuum pump in real time, and combine the on-site fault data of the fusion reactor to construct a fusion environment fault data set; (3) Perform data augmentation on the fusion environment fault data set: Generate synthetic data of minority class samples based on the k-nearest neighbor algorithm, and generate noise-resistant samples through a generative adversarial network (GAN) to expand the sample quantity and diversity of the fusion environment fault data set; (4) Perform domain adaptive joint training on the simulation environment fault data set and the enhanced fusion environment fault data set: Narrow the distribution difference between the two types of data sets through feature extraction and feature space mean alignment methods, and design a joint loss function for model optimization; (5) Based on the jointly trained model, it is used for fault diagnosis of the vacuum pump in the strong electromagnetic environment of the fusion reactor.

[0006] Optionally, the collection of the simulation environment fault data set in step (1) specifically includes: placing the faulty vacuum pump in a GTEM cell (GTEM cell: a test device for simulating the strong electromagnetic environment of a fusion reactor), applying continuous wave electromagnetic interference of 100 Hz - 50 MHz, and changing the vacuum pump load through pulse width speed regulation, and continuously collecting acceleration waveforms, current waveforms and electromagnetic field strength data at a sampling rate of 30 kHz.

[0007] Optionally, the data augmentation based on the k-nearest neighbor algorithm in step (3) is specifically: calculating the k-nearest neighbors of the minority class samples, determining the sampling multiple according to the quantity ratio of the majority class to the minority class, and generating new samples through linear interpolation: where, are the horizontal and vertical coordinates of the new sample point, are the horizontal and vertical coordinates of the original sample, are the horizontal and vertical coordinates of the neighboring points, is a random number between zero and one.

[0008] Optionally, the loss function of the feature space mean alignment in step (4) is: where, is the fusion environment fault data set, is the simulation environment fault data set, feature extraction operation, is the original data of the th sample in the fusion environment dataset, is the original data of the th sample in the simulation environment dataset, is the Frobenius norm.

[0009] Optionally, the fault types of the vacuum pump include at least one of drive motor fault, pump body air leakage fault, blade wear fault, bearing damage fault, and pump oil shortage fault.

[0010] Optionally, the generator of the generative adversarial network (GAN) is used to synthesize noisy fault signals, and the discriminator is used to distinguish real samples from synthetic samples, and the noise resistance of the generated samples is improved through adversarial training.

[0011] Optionally, the joint loss function includes a multi-classification loss term and a domain difference loss term, specifically: Among them, is the multi-classification loss of the vacuum pump fault dataset, is the difference loss between the fusion environment data and the simulation environment data. Through joint training, the total loss value is reduced to complete the training of the fault diagnosis model. is a hyperparameter. In the domain adaptation joint training, the optimal value of the hyperparameter is determined through grid search to balance the weights of the multi-classification loss and the domain difference loss.

[0012] Optionally, the test bench includes multi-class fault vacuum pumps, triaxial acceleration sensors, current transformers, broadband electromagnetic sensors, and acquisition boards.

[0013] Optionally, the sampling rate of the acquisition board is not less than 10 kHz, and it is used to continuously collect the vibration, current, and electromagnetic field signals of the vacuum pump.

[0014] Compared with the prior art, the present application includes at least one of the following beneficial technical effects: By simulating the electromagnetic environment of the fusion reactor through the GTEM cell and combining with real data training, the robustness of the model under complex electromagnetic conditions is significantly improved.

[0015] Using k-nearest neighbor interpolation and GAN to generate noise-resistant samples can effectively expand small-sample data and improve the generalization ability of the model.

[0016] Through domain adaptation joint training, the distribution difference between simulation and real data is reduced, and the cross-scene diagnosis accuracy is improved.

[0017] Adopting multi-sensor synchronous acquisition with a sampling rate of ≥10 kHz ensures signal integrity under strong interference and enhances the ability to extract fault features.

[0018] By simulating the strong electromagnetic environment of a fusion reactor and combining real operating data, the present invention innovatively solves the problem of the failure of traditional vacuum pump fault diagnosis methods under electromagnetic interference, significantly improves the adaptability and reliability of the diagnosis model, and uses data augmentation and cross-domain joint training technologies to effectively overcome the limitations of small sample data, achieving precise identification of multiple fault types, providing an important technical guarantee for the safe and stable operation of fusion devices. Brief Description of the Drawings

[0019] Figure 1 It is a flowchart of a vacuum pump fault diagnosis method for the strong electromagnetic environment of a fusion reactor. Detailed Embodiments

[0020] The technical solution of the present invention will be further described below in conjunction with the drawings and specific embodiments.

[0021] Embodiment As Figure 1 shown, a vacuum pump fault diagnosis method for the strong electromagnetic environment of a fusion reactor proposed by the present invention will be described in detail below.

[0022] I. Construction of the fault test bench: Design and manufacture multiple faulty vacuum pumps, including faulty types such as drive motor faults, pump body air leakage faults, blade wear faults, bearing damage faults, pump oil shortage faults, etc. Build a fault test bench equipped with two three-axis acceleration sensors and three current transformers for measuring the vibration acceleration signals of the vacuum pump body and the drive motor part, as well as the three-phase current signals. Equip a broadband electromagnetic sensor for collecting the electromagnetic field strength near the vacuum pump. Equip a collection host and combine a 24-bit high-precision collection board to perform continuous waveform signal collection with a sampling rate of at least 10 kHz. Design vacuum pumps with multiple faults such as drive motor faults, pump body air leakage, and blade wear to ensure that the fault diagnosis method can adapt to various failure modes that may occur in actual applications and improve the comprehensiveness of diagnosis. Equip three-axis acceleration sensors, current transformers, broadband electromagnetic sensors, and high-precision collection equipment to synchronously obtain multi-modal signals such as vibration, current, and electromagnetic environment, providing rich data support for subsequent fault feature analysis and avoiding the one-sidedness of single-signal diagnosis. Use a 24-bit high-precision collection board and a sampling rate of ≥10 kHz to ensure that signal details are accurately captured, reduce data distortion, and lay a foundation for identifying subtle differences in fault features.

[0023] II. Collection of the vacuum pump fault dataset in the fusion environment: During the steady-state operation of the fusion reactor and under other operating conditions, collect the operation datasets of various faulty vacuum pumps in the vicinity. At the same time, deploy broadband electromagnetic sensors at key positions of the vacuum pump to record the electromagnetic interference spectrum in real time and construct a dynamic baseline database of the electromagnetic environment. Through the collected vacuum pump fault data and normal datasets in the fusion experiment, obtain the fusion environment fault dataset. Collect data directly under the steady state and other operating conditions of the fusion reactor to ensure that the dataset reflects the complex electromagnetic interference and load changes in the actual operating environment and improve the adaptability of the model to the real scenario. Record the interference spectrum in real time through broadband electromagnetic sensors and establish a baseline database, which can be used to distinguish normal electromagnetic fluctuations from fault-related signal characteristics and enhance the anti-interference ability of fault identification. Collect fault and normal operation data to form a complete dataset, providing positive and negative samples for subsequent model training and avoiding diagnostic misjudgments caused by data deviation.

[0024] III. Collection of the vacuum pump fault dataset in the simulation environment: Place the vacuum pump fault test bench in the GTEM cell and use the GTEM cell test system to apply electromagnetic environments with different field strengths in a closed space. Before each experiment, the GTEM cell is not started and operates without electromagnetic interference for 10 minutes. At the same time, vacuum pumps of different fault types operate, and acceleration waveform data and current waveform data are continuously collected at a sampling rate of 30 kHz. Subsequently, the directional coupler of the GTEM cell starts to generate continuous waves of 100 Hz - 50 MHz in sequence, with a single duration of 10 minutes, and collect the operation data of different faulty vacuum pumps. Change the load of the vacuum pump by pulse width speed regulation and obtain the corresponding vibration and current data. Through the above operations, obtain the simulation fault dataset of vacuum pumps under different operating conditions in different electromagnetic environments. Using the GTEM cell to apply electromagnetic interference with different field strengths and frequency bands (100 Hz - 50 MHz) has strong controllability and can systematically analyze the influence of electromagnetic interference on different fault signals, filling the deficiency that electromagnetic interference is difficult to artificially adjust in the real fusion environment.

[0025] In this embodiment, by changing the load through pulse width speed regulation and combining the comparative collection of the non-interference (initial 10 minutes) and interference scenarios, obtain the fault data under different loads and different electromagnetic intensities, greatly increasing the diversity of the dataset and enhancing the generalization ability of the model to complex operating conditions. The 30 kHz sampling rate ensures that high-frequency signals (such as transient vibrations of bearing damage and blade wear) are effectively recorded, providing a guarantee for the extraction of high-frequency fault characteristics.

[0026] IV. Enhancement of Fusion Environment Samples: Considering that the number of fusion environment samples is much less than that of simulation environment samples, it is necessary to expand the number of samples here and enhance the noise to ensure sample diversity. To this end, a fusion environment sample space is defined. By calculating the distances of all samples in the minority class sample set, the k-nearest neighbors are obtained. According to the ratio N of the number of majority class to minority class, the sampling multiple N is obtained. For each minority class sample , several samples are randomly selected from its k-nearest neighbors. Suppose the selected ones are . By calculating any point between the sample point and the selected point as the new sample point, the calculation is as follows where are the horizontal and vertical coordinates of the new sample point, are the horizontal and vertical coordinates of the original sample, are the horizontal and vertical coordinates of the neighboring point, is a random number between zero and one. Repeat the calculation multiple times until the samples are balanced. For the minority fault samples (such as rare fault types) in the fusion environment, new samples are generated through k-nearest neighbor interpolation to balance the data volume of each category, avoid the "majority class bias" caused by sample imbalance in the model, and improve the recognition accuracy of minority fault categories. The new samples are generated on the line connecting the original sample and its neighbor, ensuring that the newly added data conforms to the manifold structure of the original data and avoiding the distribution deviation caused by randomly generated samples.

[0027] Construct a general generative adversarial network GAN, where the generator is G and the discriminator is D. The generator generates signals with noise and the fusion environment data set and inputs them into the discriminator to train the GAN. Through the trained GAN network, samples with noise resistance ability are generated and expanded into the fusion environment data set.

[0028] Domain Adaptive Training: Use the simulation environment fault data set as the auxiliary data set , and conduct joint training with the fusion environment fault data set . First, feature extraction operations need to be performed on the two data sets to obtain the feature vectors in the feature space. By aligning the means of the feature vectors of the fusion environment and simulation environment data sets in the feature space, the distribution difference between the two types of data sets is reduced.

[0029] where is the fusion environment fault data set, is the simulation environment fault data set, is the feature extraction operation, is any sample in the data set, is the feature space.

[0030] where is the sum of the number of faulty vacuum pumps and normal vacuum pumps manufactured, represents the sample belongs to the true label of the class, represents the sample belongs to the predicted probability of the class.

[0031] Among them, is the multi-classification loss of the vacuum pump fault dataset, is the difference loss between the fusion environment data and the simulation environment data. Through joint training, the total loss value is reduced to complete the training of the fault diagnosis model, is a hyperparameter, and the optimal value is obtained through grid search. Through GAN training, the generator learns the noise distribution law of the real signal and generates enhanced samples containing actual electromagnetic noise to improve the robustness of the model under strong electromagnetic interference. Supplement the extreme noise scenario data that is difficult to directly collect in the fusion environment, expand the noise coverage of the dataset, and enhance the generalization ability of the model. By aligning the mean in the feature space (calculating ), the distribution difference between the simulation environment and the fusion environment data is reduced, so that the knowledge of the simulation data (auxiliary dataset) can be effectively transferred to the real scenario, alleviating the problem of insufficient samples in the fusion environment. Combining the classification loss ( ) and the domain difference loss ( ), while ensuring the fault classification accuracy, the model is forced to learn domain-invariant features to improve the stability of cross-environment diagnosis. Determine the δ value through grid search to achieve the optimal balance of the two types of losses, avoid over-biasing towards simulation data or real data, and ensure the reliability of the model in practical applications.

[0032] In the present invention, by building a vacuum pump fault test bench with adjustable electromagnetic field strength (such as a GTEM cell), a continuous wave electromagnetic interference environment of 100 Hz - 50 MHz in a fusion reactor is simulated, and through joint training of real fusion reactor operation data and simulation data, the robustness of the fault diagnosis model under complex electromagnetic interference is significantly improved. Traditional methods are vulnerable to signal distortion interference due to not considering the influence of strong electromagnetic interference, while the present invention effectively suppresses the interference of electromagnetic noise on the diagnosis result through feature space mean alignment and anti-noise sample generation.

[0033] Aiming at the problems of few fault data samples and class imbalance in the fusion environment, the present invention proposes a linear interpolation data augmentation method based on the k-nearest neighbor algorithm and combines a generative adversarial network (GAN) to generate synthetic samples with anti-noise ability. By expanding the diversity and quantity of minority class samples, the data skew problem is greatly alleviated, enabling the model to more comprehensively learn the characteristics of different fault modes during training and improving the generalization performance.

[0034] Through the domain adaptation joint training technology, the feature spaces of the simulation environment dataset (auxiliary domain) and the fusion environment dataset (target domain) are mean-aligned, and a joint loss function including multi-classification loss ( ) and domain difference loss ( ) is designed. This method significantly reduces the difference in the distributions of the two types of data, enabling the model to make full use of the richness of the simulation data while adapting to the complexity of the real fusion environment, and improving the diagnostic accuracy in cross-domain scenarios.

[0035] A high-precision acquisition board with a sampling rate of not less than 10 kHz is adopted, combined with broadband electromagnetic sensors, triaxial acceleration sensors and current transformers, to achieve full-band synchronous acquisition of vibration, current and electromagnetic field signals. Even under strong electromagnetic interference, the signal integrity can still be ensured, providing a reliable data basis for fault feature extraction. Compared with traditional methods, the signal resolution is improved, and tiny fault features can be effectively captured.

[0036] It supports accurate diagnosis of multiple types of faults such as drive motor faults, pump body air leakage, blade wear, bearing damage and pump oil shortage. By constructing a test bench including various fault types and simulating actual working conditions through dynamic load regulation (pulse width speed regulation), the model can identify complex compound fault modes under complex working conditions, improving the diagnostic coverage rate.

[0037] The above specific embodiments are only several alternative embodiments of the present invention. Based on the technical solution of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A vacuum pump fault diagnosis method for a fusion reactor strong electromagnetic environment, characterized in that: The following steps are involved: Build a vacuum pump fault test bench with adjustable electromagnetic field strength. Use the test bench to collect vacuum pump vibration acceleration signals, three-phase current signals, and electromagnetic field strength signals under different electromagnetic field strengths to form a simulation environment fault data set. During the operation of the fusion reactor, the operation data and electromagnetic interference spectrum of the vacuum pump are collected in real time, and combined with the on-site fault data of the fusion reactor, a fusion environment fault data set is constructed; Data enhancement is performed on the fusion environment fault dataset: synthetic data of minority class samples is generated based on the k-nearest neighbor algorithm, and noise-resistant samples are generated through a generative adversarial network; The simulation environment fault dataset and the enhanced fusion environment fault dataset are jointly trained with domain adaptation: the distribution difference between the two datasets is reduced through feature extraction and feature space mean alignment methods, and a joint loss function is designed for model optimization; The jointly trained model is used to diagnose the faults of vacuum pumps in the strong electromagnetic environment of fusion reactors.

2. A vacuum pump fault diagnosis method for a fusion reactor strong electromagnetic environment according to claim 1, characterized in that: The acquisition of the simulation environment fault data set specifically includes: placing the faulty vacuum pump in a GTEM chamber, applying 100Hz-50MHz continuous wave electromagnetic interference, changing the vacuum pump load through pulse width modulation, and continuously acquiring acceleration waveform, current waveform and electromagnetic field strength data at a sampling rate of 30kHz.

3. A vacuum pump fault diagnosis method for a fusion reactor strong electromagnetic environment according to claim 1, characterized in that: The data enhancement based on the k-nearest neighbor algorithm is specifically as follows: calculating the k-nearest neighbors of the minority class samples, determining the sampling multiple according to the ratio of the number of the majority class to the minority class, and generating new samples by linear interpolation: in, is the horizontal and vertical coordinates of the new sample point, is the horizontal and vertical coordinates of the original sample, is the horizontal and vertical coordinates of the adjacent points, is a random number between zero and one.

4. A method for diagnosing vacuum pump faults in a strong electromagnetic environment of a fusion reactor according to claim 1, characterized in that: The loss function of the feature space mean alignment is: in, is the fusion environment fault dataset, is the simulation environment fault dataset, Feature extraction operations, This is the first The original data of samples, The simulation environment dataset The original data of samples, is the Frobenius norm.

5. The method for diagnosing vacuum pump faults in a strong electromagnetic environment of a fusion reactor according to claim 1, characterized in that: The failure types of the vacuum pump include at least one of a drive motor failure, a pump body air leakage failure, a blade wear failure, a bearing damage failure and a pump oil loss failure.

6. The method for diagnosing vacuum pump faults in a strong electromagnetic environment of a fusion reactor according to claim 1, characterized in that: The generator of the generative adversarial network is used to synthesize fault signals with noise, and the discriminator is used to distinguish between real samples and synthetic samples, and the noise resistance of the generated samples is improved through adversarial training.

7. The method for diagnosing vacuum pump faults in a strong electromagnetic environment of a fusion reactor according to claim 1, characterized in that: The joint loss function includes a multi-classification loss term and a domain difference loss term, specifically: in, Multi-classification loss for the vacuum pump failure dataset, The difference loss between fusion environment data and simulation environment data is reduced through joint training. Total loss value, complete the fault diagnosis model training, is a hyperparameter. In the domain adaptive joint training, the hyperparameter is determined by grid search. to balance the weights of multi-classification loss and domain difference loss.

8. The method for diagnosing vacuum pump faults in a strong electromagnetic environment of a fusion reactor according to claim 1, characterized in that: The test bench includes various types of fault vacuum pumps, a three-axis acceleration sensor, a current transformer, a broadband electromagnetic sensor and an acquisition board.

9. A method for diagnosing vacuum pump faults in a strong electromagnetic environment of a fusion reactor according to claim 8, characterized in that: The sampling rate of the acquisition board is not less than 10kHz, and is used to continuously collect the vibration, current and electromagnetic field signals of the vacuum pump.

Images