A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing
The reliability and performance of high-voltage circuit breakers (HVCBs) will directly affect the safety and stability of the power system itself, and mechanical failures of HVCBs are one of the important factors affecting the reliability of circuit breakers. Moreover, the existing fault diagnosis m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/19/2/288 |
| _version_ | 1828154118144786432 |
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| author | Lin Lin Bin Wang Jiajin Qi Lingling Chen Nantian Huang |
| author_facet | Lin Lin Bin Wang Jiajin Qi Lingling Chen Nantian Huang |
| author_sort | Lin Lin |
| collection | DOAJ |
| description | The reliability and performance of high-voltage circuit breakers (HVCBs) will directly affect the safety and stability of the power system itself, and mechanical failures of HVCBs are one of the important factors affecting the reliability of circuit breakers. Moreover, the existing fault diagnosis methods for circuit breakers are complex and inefficient in feature extraction. To improve the efficiency of feature extraction, a novel mechanical fault feature selection and diagnosis approach for high-voltage circuit breakers, using features extracted without signal processing is proposed. Firstly, the vibration signal of the HVCBs’ operating system, which collects the amplitudes of signals from normal vibration signals, is segmented by a time scale, and obviously changed. Adopting the ensemble learning method, features were extracted from each part of the divided signal, and used for constructing a vector. The Gini importance of features is obtained by random forest (RF), and the feature is ranked by the features’ importance index. After that, sequential forward selection (SFS) is applied to determine the optimal subset, while the regularized Fisher’s criterion (RFC) is used to analyze the classification ability. Then, the optimal subset is input to the hierarchical hybrid classifier, and based on a one-class support vector machine (OCSVM) and RF for fault diagnosis, the state is accurately recognized by OCSVM. The known fault types are identified using RF, and the identification results are calibrated with OCSVM of a particular fault type. The experimental proves that the new method has high feature extraction efficiency and recognition accuracy by the measured HVCBs vibration signal, while the unknown fault type data of the untrained samples is effectively identified. |
| first_indexed | 2024-04-11T22:35:46Z |
| format | Article |
| id | doaj.art-62e41ebfd6354b3c982c2f9e6979b176 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:35:46Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-62e41ebfd6354b3c982c2f9e6979b1762022-12-22T03:59:13ZengMDPI AGSensors1424-82202019-01-0119228810.3390/s19020288s19020288A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal ProcessingLin Lin0Bin Wang1Jiajin Qi2Lingling Chen3Nantian Huang4College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaTaian Power Supply Company, State Grid Shandong Electric Power Company, Taian 271000, ChinaHangzhou Municipal Electric Power Supply Company of State Grid, Hangzhou 310009, ChinaCollege of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaThe reliability and performance of high-voltage circuit breakers (HVCBs) will directly affect the safety and stability of the power system itself, and mechanical failures of HVCBs are one of the important factors affecting the reliability of circuit breakers. Moreover, the existing fault diagnosis methods for circuit breakers are complex and inefficient in feature extraction. To improve the efficiency of feature extraction, a novel mechanical fault feature selection and diagnosis approach for high-voltage circuit breakers, using features extracted without signal processing is proposed. Firstly, the vibration signal of the HVCBs’ operating system, which collects the amplitudes of signals from normal vibration signals, is segmented by a time scale, and obviously changed. Adopting the ensemble learning method, features were extracted from each part of the divided signal, and used for constructing a vector. The Gini importance of features is obtained by random forest (RF), and the feature is ranked by the features’ importance index. After that, sequential forward selection (SFS) is applied to determine the optimal subset, while the regularized Fisher’s criterion (RFC) is used to analyze the classification ability. Then, the optimal subset is input to the hierarchical hybrid classifier, and based on a one-class support vector machine (OCSVM) and RF for fault diagnosis, the state is accurately recognized by OCSVM. The known fault types are identified using RF, and the identification results are calibrated with OCSVM of a particular fault type. The experimental proves that the new method has high feature extraction efficiency and recognition accuracy by the measured HVCBs vibration signal, while the unknown fault type data of the untrained samples is effectively identified.http://www.mdpi.com/1424-8220/19/2/288high voltage circuit breakerone-class support vector machinerandom forest |
| spellingShingle | Lin Lin Bin Wang Jiajin Qi Lingling Chen Nantian Huang A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing Sensors high voltage circuit breaker one-class support vector machine random forest |
| title | A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing |
| title_full | A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing |
| title_fullStr | A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing |
| title_full_unstemmed | A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing |
| title_short | A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing |
| title_sort | novel mechanical fault feature selection and diagnosis approach for high voltage circuit breakers using features extracted without signal processing |
| topic | high voltage circuit breaker one-class support vector machine random forest |
| url | http://www.mdpi.com/1424-8220/19/2/288 |
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