A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems
A main circuit ground fault (MCGF) is a typical system fault in an electrical traction drive system (ETDS). When two or more MCGFs occur, it will cause serious accidents. Therefore, it is particularly important to detect and handle MCGFs in a timely manner. To improve the efficiency of train operati...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Vehicles |
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| Online Access: | https://www.mdpi.com/2624-8921/6/4/91 |
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| author | Xinyao Hou Juntong Liu Jinxin Zhang Qiang Ni |
| author_facet | Xinyao Hou Juntong Liu Jinxin Zhang Qiang Ni |
| author_sort | Xinyao Hou |
| collection | DOAJ |
| description | A main circuit ground fault (MCGF) is a typical system fault in an electrical traction drive system (ETDS). When two or more MCGFs occur, it will cause serious accidents. Therefore, it is particularly important to detect and handle MCGFs in a timely manner. To improve the efficiency of train operation and ensure driving safety, this paper proposes a hybrid data-driven MCGF diagnosis method. First, the voltage signals related to the fault are selected according to the mechanism analysis of the MCGF, and then the initial feature variables are constructed according to these voltage signals. Secondly, the initial feature variables of different types of MCGF are analyzed in the time and frequency domains by wavelet transform, and four feature indicators are calculated. Finally, the fault feature indicators are trained by random forest to obtain a model for subsequent fault diagnosis. After comparative experiments using various machine learning methods, it was found that the RF used in the proposed method has a better diagnostic effect, and the correct isolation rate exceeds 99%. |
| format | Article |
| id | doaj-art-c94dadf046a840848f797e2b519e4072 |
| institution | DOAJ |
| issn | 2624-8921 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vehicles |
| spelling | doaj-art-c94dadf046a840848f797e2b519e40722025-08-20T02:43:50ZengMDPI AGVehicles2624-89212024-11-01641872188510.3390/vehicles6040091A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive SystemsXinyao Hou0Juntong Liu1Jinxin Zhang2Qiang Ni3School of Locomotives and Rolling Stock, Guangzhou Railway Polytechnic, Guangzhou 511300, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaA main circuit ground fault (MCGF) is a typical system fault in an electrical traction drive system (ETDS). When two or more MCGFs occur, it will cause serious accidents. Therefore, it is particularly important to detect and handle MCGFs in a timely manner. To improve the efficiency of train operation and ensure driving safety, this paper proposes a hybrid data-driven MCGF diagnosis method. First, the voltage signals related to the fault are selected according to the mechanism analysis of the MCGF, and then the initial feature variables are constructed according to these voltage signals. Secondly, the initial feature variables of different types of MCGF are analyzed in the time and frequency domains by wavelet transform, and four feature indicators are calculated. Finally, the fault feature indicators are trained by random forest to obtain a model for subsequent fault diagnosis. After comparative experiments using various machine learning methods, it was found that the RF used in the proposed method has a better diagnostic effect, and the correct isolation rate exceeds 99%.https://www.mdpi.com/2624-8921/6/4/91electrical traction drive system (ETDS)main circuit ground fault (MCGF)fault diagnosiswavelet transformrandom forest (RF) |
| spellingShingle | Xinyao Hou Juntong Liu Jinxin Zhang Qiang Ni A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems Vehicles electrical traction drive system (ETDS) main circuit ground fault (MCGF) fault diagnosis wavelet transform random forest (RF) |
| title | A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems |
| title_full | A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems |
| title_fullStr | A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems |
| title_full_unstemmed | A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems |
| title_short | A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems |
| title_sort | hybrid data driven method for main circuit gound faults diagnosis in electrical traction drive systems |
| topic | electrical traction drive system (ETDS) main circuit ground fault (MCGF) fault diagnosis wavelet transform random forest (RF) |
| url | https://www.mdpi.com/2624-8921/6/4/91 |
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