PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural Networks
Due to the complexity of fault states and the non-linear relationship between input and output responses, fault diagnosis in complex power circuit systems faces significant challenges. This study proposes a novel hybrid method, PW-FBPNN, which integrates principal component analysis (PCA), wavelet p...
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| Format: | Article |
| Language: | English |
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University of Zagreb Faculty of Electrical Engineering and Computing
2024-01-01
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| Series: | Journal of Computing and Information Technology |
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| Online Access: | https://hrcak.srce.hr/file/471974 |
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| author | Xu Chen Chao Zhang Haomiao Zhang Zhiqiang Cheng Yu Yan |
| author_facet | Xu Chen Chao Zhang Haomiao Zhang Zhiqiang Cheng Yu Yan |
| author_sort | Xu Chen |
| collection | DOAJ |
| description | Due to the complexity of fault states and the non-linear relationship between input and output responses, fault diagnosis in complex power circuit systems faces significant challenges. This study proposes a novel hybrid method, PW-FBPNN, which integrates principal component analysis (PCA), wavelet packet transform (WPT), and fuzzy back propagation neural network (FBPNN) to enhance fault diagnosis. The effectiveness of this method was demonstrated through experiments on the voltage divider basic operational amplifier and the second-order filter circuit of the four operational amplifiers. PW-FBPNN achieved 100% accuracy in diagnosing most types of faults, with a minimum accuracy of 91.67% for challenging faults. This method was significantly superior to existing methods such as FCM-HMM-SVM and KICA-DNN in terms of accuracy and computational efficiency and could complete the diagnosis in just 0.01 seconds. These results indicate that PW-FBPNN has the potential to improve fault diagnosis in power circuit systems, providing a promising solution for enhancing system reliability and maintenance efficiency. |
| format | Article |
| id | doaj-art-ac82ece6db014160a8ef1a696024a2ac |
| institution | Kabale University |
| issn | 1846-3908 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb Faculty of Electrical Engineering and Computing |
| record_format | Article |
| series | Journal of Computing and Information Technology |
| spelling | doaj-art-ac82ece6db014160a8ef1a696024a2ac2025-01-09T14:17:08ZengUniversity of Zagreb Faculty of Electrical Engineering and ComputingJournal of Computing and Information Technology1846-39082024-01-0132315917610.20532/cit.2024.1005850PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural NetworksXu Chen0Chao Zhang1Haomiao Zhang2Zhiqiang Cheng3Yu Yan4State Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaState Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaState Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaState Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaState Grid Ningxia Marketing Service Center, State Grid Ningxia Metrology Center, Yinchuan, Ningxia, ChinaDue to the complexity of fault states and the non-linear relationship between input and output responses, fault diagnosis in complex power circuit systems faces significant challenges. This study proposes a novel hybrid method, PW-FBPNN, which integrates principal component analysis (PCA), wavelet packet transform (WPT), and fuzzy back propagation neural network (FBPNN) to enhance fault diagnosis. The effectiveness of this method was demonstrated through experiments on the voltage divider basic operational amplifier and the second-order filter circuit of the four operational amplifiers. PW-FBPNN achieved 100% accuracy in diagnosing most types of faults, with a minimum accuracy of 91.67% for challenging faults. This method was significantly superior to existing methods such as FCM-HMM-SVM and KICA-DNN in terms of accuracy and computational efficiency and could complete the diagnosis in just 0.01 seconds. These results indicate that PW-FBPNN has the potential to improve fault diagnosis in power circuit systems, providing a promising solution for enhancing system reliability and maintenance efficiency.https://hrcak.srce.hr/file/471974power circuit systemfuzzy neural networkprincipal component analysiswavelet packet transform methodfault diagnosis |
| spellingShingle | Xu Chen Chao Zhang Haomiao Zhang Zhiqiang Cheng Yu Yan PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural Networks Journal of Computing and Information Technology power circuit system fuzzy neural network principal component analysis wavelet packet transform method fault diagnosis |
| title | PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural Networks |
| title_full | PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural Networks |
| title_fullStr | PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural Networks |
| title_full_unstemmed | PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural Networks |
| title_short | PW-FBPNN: A Hybrid Fault Diagnosis Method for Power Circuit Systems Combining Principal Component Analysis, Wavelet Packet Transform, and Fuzzy Neural Networks |
| title_sort | pw fbpnn a hybrid fault diagnosis method for power circuit systems combining principal component analysis wavelet packet transform and fuzzy neural networks |
| topic | power circuit system fuzzy neural network principal component analysis wavelet packet transform method fault diagnosis |
| url | https://hrcak.srce.hr/file/471974 |
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