Diagnosis of Interturn Short-Circuit Faults in Permanent Magnet Synchronous Motors Based on Few-Shot Learning Under a Federated Learning Framework
A large amount of labeled data are important to enhance the performance of deep-learning-based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality samples in real industrial applications, federated learning is an effective framework for solving the problem of spars...
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| Vydáno v: | IEEE transactions on industrial informatics Ročník 17; číslo 12; s. 8495 - 8504 |
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IEEE
01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1551-3203, 1941-0050 |
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| Abstract | A large amount of labeled data are important to enhance the performance of deep-learning-based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality samples in real industrial applications, federated learning is an effective framework for solving the problem of sparse samples by using the distributed data. Its global model is updated by the local client without sharing data at each round. Considering computing resources and communication loss of multiple clients, an efficient method based on stacked sparse autoencoders (SSAEs) and Siamese networks is proposed to detect interturn short-circuit (ITSC) faults in permanent magnet synchronous motors. In this article, to achieve an accurate ITSC fault detection, an SSAE was employed to extract sparse features in a limited number of samples, and Siamese networks were used to determine the similarity between the given samples. The problem of fault diagnosis is transformed into a classification problem under few-shot learning. Furthermore, the proposed method is trained successfully in the frameworks of centralized learning and decentralized structure. The experimental results indicate that the proposed method achieved high fault diagnosis accuracy. Moreover, it is suitable for deployment in smart manufacturing systems. |
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| AbstractList | A large amount of labeled data are important to enhance the performance of deep-learning-based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality samples in real industrial applications, federated learning is an effective framework for solving the problem of sparse samples by using the distributed data. Its global model is updated by the local client without sharing data at each round. Considering computing resources and communication loss of multiple clients, an efficient method based on stacked sparse autoencoders (SSAEs) and Siamese networks is proposed to detect interturn short-circuit (ITSC) faults in permanent magnet synchronous motors. In this article, to achieve an accurate ITSC fault detection, an SSAE was employed to extract sparse features in a limited number of samples, and Siamese networks were used to determine the similarity between the given samples. The problem of fault diagnosis is transformed into a classification problem under few-shot learning. Furthermore, the proposed method is trained successfully in the frameworks of centralized learning and decentralized structure. The experimental results indicate that the proposed method achieved high fault diagnosis accuracy. Moreover, it is suitable for deployment in smart manufacturing systems. |
| Author | Wang, Yanbo Zhang, Yi Zhang, Jinglin Zhu, Kai Li, Yuanjiang |
| Author_xml | – sequence: 1 givenname: Jinglin orcidid: 0000-0001-7499-1992 surname: Zhang fullname: Zhang, Jinglin email: jinglin.zhang37@gmail.com organization: School of Artificial Intelligence, Hebei University of Technology, Tianjin, China – sequence: 2 givenname: Yanbo orcidid: 0000-0002-0787-7361 surname: Wang fullname: Wang, Yanbo email: 243334258@qq.com organization: Jiangsu University of Science and Technology, Zhenjiang, China – sequence: 3 givenname: Kai orcidid: 0000-0003-1693-0336 surname: Zhu fullname: Zhu, Kai email: fatkyo@jsut.edu.cn organization: Jiangsu University of Technology, Changzhou, China – sequence: 4 givenname: Yi surname: Zhang fullname: Zhang, Yi email: 273086243@qq.com organization: Jiangsu University of Science and Technology, Zhenjiang, China – sequence: 5 givenname: Yuanjiang orcidid: 0000-0003-3959-8065 surname: Li fullname: Li, Yuanjiang email: liyuanjiang@just.edu.cn organization: Jiangsu University of Science and Technology, Zhenjiang, China |
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| SubjectTerms | Artificial neural networks Circuit faults Data models Deep learning Fault detection Fault diagnosis Feature extraction Federated learning federated learning (FL) few-shot learning Industrial applications interturn short circuit (ITSC) Machine learning permanent magnet synchronous motor (PMSM) Permanent magnets Short circuits Smart manufacturing stacked sparse autoencoder (SSAE) Synchronous motors Training |
| Title | Diagnosis of Interturn Short-Circuit Faults in Permanent Magnet Synchronous Motors Based on Few-Shot Learning Under a Federated Learning Framework |
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