Power Converter Fault Detection Using MLCA–SpikingShuffleNet
With the widespread adoption of electric vehicles, the power converter, as a key component, plays a crucial role. Traditional fault detection methods often face challenges in real-time performance and computational efficiency, making it difficult to meet the demands of electric vehicle power convert...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/1/36 |
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| author | Li Wang Feiyang Zhu Fengfan Jiang Yuwei Yang |
| author_facet | Li Wang Feiyang Zhu Fengfan Jiang Yuwei Yang |
| author_sort | Li Wang |
| collection | DOAJ |
| description | With the widespread adoption of electric vehicles, the power converter, as a key component, plays a crucial role. Traditional fault detection methods often face challenges in real-time performance and computational efficiency, making it difficult to meet the demands of electric vehicle power converters for efficient and accurate fault diagnosis. To address this challenge, this paper proposes a novel fault detection model—SpikingShuffleNet. This paper first designs an efficient SpikingShuffle Unit that integrates grouped convolutions and channel shuffle techniques, effectively reducing the model’s computational complexity by optimizing feature extraction and channel interaction. Next, by appropriately stacking SpikingShuffle Units and refining the network architecture, a complete lightweight diagnostic network is constructed for real-time fault detection in electric vehicle power converters. Finally, the Mixed Local Channel Attention mechanism is introduced to address the potential limitations in feature representation caused by grouped convolutions, further enhancing fault detection accuracy and robustness by balancing local detail preservation and global feature integration. Experimental results show that SpikingShuffleNet exhibits excellent accuracy and robustness in the fault detection task for power converters, fulfilling the real-time fault diagnosis requirements for low-power embedded devices. |
| format | Article |
| id | doaj-art-6a24d9f089bb44fa9d00c42372d3165b |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-6a24d9f089bb44fa9d00c42372d3165b2025-01-24T13:52:50ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-01-011613610.3390/wevj16010036Power Converter Fault Detection Using MLCA–SpikingShuffleNetLi Wang0Feiyang Zhu1Fengfan Jiang2Yuwei Yang3School of Electrical Engineering and Automation, Nantong University, Nantong 226019, ChinaSchool of Electrical Engineering and Automation, Nantong University, Nantong 226019, ChinaSchool of Electrical Engineering and Automation, Nantong University, Nantong 226019, ChinaSchool of Electrical Engineering and Automation, Nantong University, Nantong 226019, ChinaWith the widespread adoption of electric vehicles, the power converter, as a key component, plays a crucial role. Traditional fault detection methods often face challenges in real-time performance and computational efficiency, making it difficult to meet the demands of electric vehicle power converters for efficient and accurate fault diagnosis. To address this challenge, this paper proposes a novel fault detection model—SpikingShuffleNet. This paper first designs an efficient SpikingShuffle Unit that integrates grouped convolutions and channel shuffle techniques, effectively reducing the model’s computational complexity by optimizing feature extraction and channel interaction. Next, by appropriately stacking SpikingShuffle Units and refining the network architecture, a complete lightweight diagnostic network is constructed for real-time fault detection in electric vehicle power converters. Finally, the Mixed Local Channel Attention mechanism is introduced to address the potential limitations in feature representation caused by grouped convolutions, further enhancing fault detection accuracy and robustness by balancing local detail preservation and global feature integration. Experimental results show that SpikingShuffleNet exhibits excellent accuracy and robustness in the fault detection task for power converters, fulfilling the real-time fault diagnosis requirements for low-power embedded devices.https://www.mdpi.com/2032-6653/16/1/36power converterfault detectiondepth-wise convolutionchannel shufflemixed local channel attentionembedded devices |
| spellingShingle | Li Wang Feiyang Zhu Fengfan Jiang Yuwei Yang Power Converter Fault Detection Using MLCA–SpikingShuffleNet World Electric Vehicle Journal power converter fault detection depth-wise convolution channel shuffle mixed local channel attention embedded devices |
| title | Power Converter Fault Detection Using MLCA–SpikingShuffleNet |
| title_full | Power Converter Fault Detection Using MLCA–SpikingShuffleNet |
| title_fullStr | Power Converter Fault Detection Using MLCA–SpikingShuffleNet |
| title_full_unstemmed | Power Converter Fault Detection Using MLCA–SpikingShuffleNet |
| title_short | Power Converter Fault Detection Using MLCA–SpikingShuffleNet |
| title_sort | power converter fault detection using mlca spikingshufflenet |
| topic | power converter fault detection depth-wise convolution channel shuffle mixed local channel attention embedded devices |
| url | https://www.mdpi.com/2032-6653/16/1/36 |
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