Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM
In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local fea...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/1/124 |
| _version_ | 1797358218251337728 |
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| author | Qinzhe Liu Xiaolong Wang Zhaojing Guo Jian Li Wei Xu Xiaowen Dai Chenlei Liu Tong Zhao |
| author_facet | Qinzhe Liu Xiaolong Wang Zhaojing Guo Jian Li Wei Xu Xiaowen Dai Chenlei Liu Tong Zhao |
| author_sort | Qinzhe Liu |
| collection | DOAJ |
| description | In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker’s operating mechanism. In this study, the 72.5 kV SF<sub>6</sub> circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms. |
| first_indexed | 2024-03-08T14:57:42Z |
| format | Article |
| id | doaj.art-c073e7cf35c94c6f8afa10644db59133 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:57:42Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c073e7cf35c94c6f8afa10644db591332024-01-10T15:08:40ZengMDPI AGSensors1424-82202023-12-0124112410.3390/s24010124Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELMQinzhe Liu0Xiaolong Wang1Zhaojing Guo2Jian Li3Wei Xu4Xiaowen Dai5Chenlei Liu6Tong Zhao7School of Electrical Engineering, Shangdong University, Jinan 250100, ChinaSchool of Electrical Engineering, Shangdong University, Jinan 250100, ChinaTaikai Automation Co., Ltd., Taian 271000, ChinaTaikai Disconnector Co., Ltd., Taian 271000, ChinaTaikai Automation Co., Ltd., Taian 271000, ChinaSchool of Electrical Engineering, Shangdong University, Jinan 250100, ChinaSchool of Electrical Engineering, Shangdong University, Jinan 250100, ChinaSchool of Electrical Engineering, Shangdong University, Jinan 250100, ChinaIn response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker’s operating mechanism. In this study, the 72.5 kV SF<sub>6</sub> circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms.https://www.mdpi.com/1424-8220/24/1/124breakervibration signalS-transformgray level co-occurrence matrixextreme learning machine |
| spellingShingle | Qinzhe Liu Xiaolong Wang Zhaojing Guo Jian Li Wei Xu Xiaowen Dai Chenlei Liu Tong Zhao Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM Sensors breaker vibration signal S-transform gray level co-occurrence matrix extreme learning machine |
| title | Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM |
| title_full | Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM |
| title_fullStr | Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM |
| title_full_unstemmed | Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM |
| title_short | Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM |
| title_sort | research on circuit breaker operating mechanism fault diagnosis method combining global local feature extraction and kelm |
| topic | breaker vibration signal S-transform gray level co-occurrence matrix extreme learning machine |
| url | https://www.mdpi.com/1424-8220/24/1/124 |
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