Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model
Electric shock protection is critical for ensuring power safety in low-voltage grids, and robust fault diagnosis methods provide an essential foundation for the accurate operation of such protection devices. However, current low-voltage electric shock protection devices often suffer from limitations...
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/24/3984 |
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| author | Meijin Lin Yuliang Luo Senjie Chen Zhirong Qiu Zibin Dai |
| author_facet | Meijin Lin Yuliang Luo Senjie Chen Zhirong Qiu Zibin Dai |
| author_sort | Meijin Lin |
| collection | DOAJ |
| description | Electric shock protection is critical for ensuring power safety in low-voltage grids, and robust fault diagnosis methods provide an essential foundation for the accurate operation of such protection devices. However, current low-voltage electric shock protection devices often suffer from limitations in operational precision and in their ability to effectively recognize electric shock types. To address these challenges, this paper proposes a fault diagnosis method for low-voltage electric shocks based on an attention-enhanced parallel CNN-BiLSTM model. The method first utilizes CNN to extract local spatial features of the electric shock signal and BiLSTM to capture temporal features. An attention mechanism is then introduced to fuse the local spatial and temporal features with weighted emphasis. Finally, a fully connected layer maps the fused features to the output layer, generating diagnostic results. Visualization through T-SNE analysis validates the improvement in model performance due to the attention mechanism. Comparative experiments show that the proposed model outperforms single models and other combined models in terms of accuracy, precision, recall, F1 score, and convergence speed. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 99.55%. |
| format | Article |
| id | doaj-art-03250c62e1b143a98ca84c5735e6d056 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-03250c62e1b143a98ca84c5735e6d0562025-08-20T02:00:29ZengMDPI AGMathematics2227-73902024-12-011224398410.3390/math12243984Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory ModelMeijin Lin0Yuliang Luo1Senjie Chen2Zhirong Qiu3Zibin Dai4School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, ChinaSchool of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, ChinaSchool of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, ChinaSchool of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, ChinaSchool of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, ChinaElectric shock protection is critical for ensuring power safety in low-voltage grids, and robust fault diagnosis methods provide an essential foundation for the accurate operation of such protection devices. However, current low-voltage electric shock protection devices often suffer from limitations in operational precision and in their ability to effectively recognize electric shock types. To address these challenges, this paper proposes a fault diagnosis method for low-voltage electric shocks based on an attention-enhanced parallel CNN-BiLSTM model. The method first utilizes CNN to extract local spatial features of the electric shock signal and BiLSTM to capture temporal features. An attention mechanism is then introduced to fuse the local spatial and temporal features with weighted emphasis. Finally, a fully connected layer maps the fused features to the output layer, generating diagnostic results. Visualization through T-SNE analysis validates the improvement in model performance due to the attention mechanism. Comparative experiments show that the proposed model outperforms single models and other combined models in terms of accuracy, precision, recall, F1 score, and convergence speed. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 99.55%.https://www.mdpi.com/2227-7390/12/24/3984deep learningfault diagnosislow-voltage power gridattention mechanismbiological electric shock |
| spellingShingle | Meijin Lin Yuliang Luo Senjie Chen Zhirong Qiu Zibin Dai Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model Mathematics deep learning fault diagnosis low-voltage power grid attention mechanism biological electric shock |
| title | Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model |
| title_full | Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model |
| title_fullStr | Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model |
| title_full_unstemmed | Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model |
| title_short | Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model |
| title_sort | low voltage biological electric shock fault diagnosis based on the attention mechanism fusion parallel convolutional neural network bidirectional long short term memory model |
| topic | deep learning fault diagnosis low-voltage power grid attention mechanism biological electric shock |
| url | https://www.mdpi.com/2227-7390/12/24/3984 |
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