Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder
As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are cro...
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MDPI AG
2021-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/3/339 |
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| author | Xiaowei Xu Jingyi Feng Liu Zhan Zhixiong Li Feng Qian Yunbing Yan |
| author_facet | Xiaowei Xu Jingyi Feng Liu Zhan Zhixiong Li Feng Qian Yunbing Yan |
| author_sort | Xiaowei Xu |
| collection | DOAJ |
| description | As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features. |
| first_indexed | 2024-03-10T13:17:11Z |
| format | Article |
| id | doaj.art-30959b5ab7b94ef69c1f0d73c6948ae7 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:17:11Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-30959b5ab7b94ef69c1f0d73c6948ae72023-11-21T10:18:31ZengMDPI AGEntropy1099-43002021-03-0123333910.3390/e23030339Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising AutoencoderXiaowei Xu0Jingyi Feng1Liu Zhan2Zhixiong Li3Feng Qian4Yunbing Yan5School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaYonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, KoreaSchool of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, ChinaAs a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features.https://www.mdpi.com/1099-4300/23/3/339stacked denoising autoencoderpermanent magnet synchronous motorsupport vector machinefault diagnosis |
| spellingShingle | Xiaowei Xu Jingyi Feng Liu Zhan Zhixiong Li Feng Qian Yunbing Yan Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder Entropy stacked denoising autoencoder permanent magnet synchronous motor support vector machine fault diagnosis |
| title | Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder |
| title_full | Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder |
| title_fullStr | Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder |
| title_full_unstemmed | Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder |
| title_short | Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder |
| title_sort | fault diagnosis of permanent magnet synchronous motor based on stacked denoising autoencoder |
| topic | stacked denoising autoencoder permanent magnet synchronous motor support vector machine fault diagnosis |
| url | https://www.mdpi.com/1099-4300/23/3/339 |
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