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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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 17; H. 12; S. 8495 - 8504
Hauptverfasser: Zhang, Jinglin, Wang, Yanbo, Zhu, Kai, Zhang, Yi, Li, Yuanjiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
ISSN:1551-3203, 1941-0050
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Zusammenfassung: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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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3067915