T-type inverter fault diagnosis based on GASF and improved AlexNet
Aiming at the problems of high similarity of T-type inverter open-circuit fault features, cumbersome manual extraction of fault features, and the inability of one-dimensional convolutional neural network to fully play the role of feature extraction. In this paper, an end-to-end fault diagnosis metho...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723001038 |
| _version_ | 1827902777424084992 |
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| author | Yabo Cui Rongjie Wang Yupeng Si Shiqi Zhang Yichun Wang Anhui Lin |
| author_facet | Yabo Cui Rongjie Wang Yupeng Si Shiqi Zhang Yichun Wang Anhui Lin |
| author_sort | Yabo Cui |
| collection | DOAJ |
| description | Aiming at the problems of high similarity of T-type inverter open-circuit fault features, cumbersome manual extraction of fault features, and the inability of one-dimensional convolutional neural network to fully play the role of feature extraction. In this paper, an end-to-end fault diagnosis method is proposed, which is based on Gramian Angular Summation Field and improved AlexNet network. The fault results can be diagnosed by collecting only the single-phase line voltage. Firstly, the collected one-dimensional timing signal is mapped into two-dimensional images through the Gram summation angle field algorithm. Then feature extraction is performed by the improved AlexNet to take advantage of its ability to extract image features. Finally, the fault diagnosis result is output by the Softmax layer. Simulation experiments show that the method proposed in this paper can automatically extract features that are helpful for fault identification from raw data. The fault diagnosis rate of this model is as high as 99.72%, and it can diagnose not only a single fault, but also multiple faults in different phases. Compared with other methods, the proposed method shows a better fault feature extraction effect and higher fault diagnosis accuracy. |
| first_indexed | 2024-03-13T00:03:59Z |
| format | Article |
| id | doaj.art-83d12446d9ae4bfe97f9a5291006b8c0 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T00:03:59Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-83d12446d9ae4bfe97f9a5291006b8c02023-07-13T05:29:19ZengElsevierEnergy Reports2352-48472023-12-01927182731T-type inverter fault diagnosis based on GASF and improved AlexNetYabo Cui0Rongjie Wang1Yupeng Si2Shiqi Zhang3Yichun Wang4Anhui Lin5School of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering, Xiamen, 361021, China; Corresponding author. No. 176 Shigu Road, Jimei District, Xiamen 361021, China.School of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Engineering, Jimei University, Xiamen 361021, ChinaAiming at the problems of high similarity of T-type inverter open-circuit fault features, cumbersome manual extraction of fault features, and the inability of one-dimensional convolutional neural network to fully play the role of feature extraction. In this paper, an end-to-end fault diagnosis method is proposed, which is based on Gramian Angular Summation Field and improved AlexNet network. The fault results can be diagnosed by collecting only the single-phase line voltage. Firstly, the collected one-dimensional timing signal is mapped into two-dimensional images through the Gram summation angle field algorithm. Then feature extraction is performed by the improved AlexNet to take advantage of its ability to extract image features. Finally, the fault diagnosis result is output by the Softmax layer. Simulation experiments show that the method proposed in this paper can automatically extract features that are helpful for fault identification from raw data. The fault diagnosis rate of this model is as high as 99.72%, and it can diagnose not only a single fault, but also multiple faults in different phases. Compared with other methods, the proposed method shows a better fault feature extraction effect and higher fault diagnosis accuracy.http://www.sciencedirect.com/science/article/pii/S2352484723001038T-type inverterGASFImproved AlexNetFault diagnosis |
| spellingShingle | Yabo Cui Rongjie Wang Yupeng Si Shiqi Zhang Yichun Wang Anhui Lin T-type inverter fault diagnosis based on GASF and improved AlexNet Energy Reports T-type inverter GASF Improved AlexNet Fault diagnosis |
| title | T-type inverter fault diagnosis based on GASF and improved AlexNet |
| title_full | T-type inverter fault diagnosis based on GASF and improved AlexNet |
| title_fullStr | T-type inverter fault diagnosis based on GASF and improved AlexNet |
| title_full_unstemmed | T-type inverter fault diagnosis based on GASF and improved AlexNet |
| title_short | T-type inverter fault diagnosis based on GASF and improved AlexNet |
| title_sort | t type inverter fault diagnosis based on gasf and improved alexnet |
| topic | T-type inverter GASF Improved AlexNet Fault diagnosis |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723001038 |
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