Optimization method for remaining life prediction of conventional circuit breaker based on deep learning
In the context of smart grid, aiming at the condition monitoring of conventional circuit breakers with complex mechanical actions, an optimization method for remaining life prediction of conventional circuit breaker based on deep learning is proposed. Firstly, the variational mode decomposition (VMD...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-05-01
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| Series: | Diance yu yibiao |
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| Online Access: | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220701008&flag=1&journal_id=dcyyben&year_id=2025 |
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| _version_ | 1849467702209413120 |
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| author | SUN Shuguang WEI Shuo WANG Jingqin SHAO Xu SUN Liang GAO Hui |
| author_facet | SUN Shuguang WEI Shuo WANG Jingqin SHAO Xu SUN Liang GAO Hui |
| author_sort | SUN Shuguang |
| collection | DOAJ |
| description | In the context of smart grid, aiming at the condition monitoring of conventional circuit breakers with complex mechanical actions, an optimization method for remaining life prediction of conventional circuit breaker based on deep learning is proposed. Firstly, the variational mode decomposition (VMD) is used to decompose the opening vibration signal, and the mode with larger kurtosis is selected for reconstruction to highlight the effective shock characteristics of the signal. Then, the feature attention convolutional neural network (FACNN) is introduced for life prediction, and the feature attention module is embedded in the one-dimensional convolution layer to optimize the ability of neurons to capture key state information. Finally, the measured data of the circuit breaker is used for verification. The results show that the method can realize the prediction of the remaining mechanical life of circuit breakers in a targeted manner, and has a high prediction accuracy and stability, which effectively reduces the influence of data uncertainty caused by the complexity of the system. |
| format | Article |
| id | doaj-art-cf7d48065b9f403db0099b775cdfad00 |
| institution | Kabale University |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-cf7d48065b9f403db0099b775cdfad002025-08-20T03:26:05ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-05-0162520020710.19753/j.issn1001-1390.2025.05.0241001-1390(2025)05-0200-08Optimization method for remaining life prediction of conventional circuit breaker based on deep learningSUN Shuguang0WEI Shuo1WANG Jingqin2SHAO Xu3SUN Liang4GAO Hui5School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, ChinaBeijing Beiyuan Electric Co., Ltd., Beijing 101105, ChinaIn the context of smart grid, aiming at the condition monitoring of conventional circuit breakers with complex mechanical actions, an optimization method for remaining life prediction of conventional circuit breaker based on deep learning is proposed. Firstly, the variational mode decomposition (VMD) is used to decompose the opening vibration signal, and the mode with larger kurtosis is selected for reconstruction to highlight the effective shock characteristics of the signal. Then, the feature attention convolutional neural network (FACNN) is introduced for life prediction, and the feature attention module is embedded in the one-dimensional convolution layer to optimize the ability of neurons to capture key state information. Finally, the measured data of the circuit breaker is used for verification. The results show that the method can realize the prediction of the remaining mechanical life of circuit breakers in a targeted manner, and has a high prediction accuracy and stability, which effectively reduces the influence of data uncertainty caused by the complexity of the system.http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220701008&flag=1&journal_id=dcyyben&year_id=2025conventional circuit breakerremaining life predictionvmdfeature attentioncnn |
| spellingShingle | SUN Shuguang WEI Shuo WANG Jingqin SHAO Xu SUN Liang GAO Hui Optimization method for remaining life prediction of conventional circuit breaker based on deep learning Diance yu yibiao conventional circuit breaker remaining life prediction vmd feature attention cnn |
| title | Optimization method for remaining life prediction of conventional circuit breaker based on deep learning |
| title_full | Optimization method for remaining life prediction of conventional circuit breaker based on deep learning |
| title_fullStr | Optimization method for remaining life prediction of conventional circuit breaker based on deep learning |
| title_full_unstemmed | Optimization method for remaining life prediction of conventional circuit breaker based on deep learning |
| title_short | Optimization method for remaining life prediction of conventional circuit breaker based on deep learning |
| title_sort | optimization method for remaining life prediction of conventional circuit breaker based on deep learning |
| topic | conventional circuit breaker remaining life prediction vmd feature attention cnn |
| url | http://www.emijournal.net/dcyyben/ch/reader/create_pdf.aspx?file_no=20220701008&flag=1&journal_id=dcyyben&year_id=2025 |
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