A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current
Railway traction motor bearings (RTMB) are critical components in high-speed trains (HST) that are particularly susceptible to failure due to the high stress and rotational frequency they experience. To address the challenge of high false-positive rates in existing monitoring systems, this paper int...
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| author | Qi Sun Juan Zhu Chunjun Chen |
| author_facet | Qi Sun Juan Zhu Chunjun Chen |
| author_sort | Qi Sun |
| collection | DOAJ |
| description | Railway traction motor bearings (RTMB) are critical components in high-speed trains (HST) that are particularly susceptible to failure due to the high stress and rotational frequency they experience. To address the challenge of high false-positive rates in existing monitoring systems, this paper introduces a novel sensorless monitoring scheme that leverages stator current to detect fault-related characteristics, eliminating the need for additional sensors. This approach employs a hybrid signal preprocessing algorithm that integrates adaptive notch filtering (ANF) with envelope spectrum analysis (ESA) to effectively sparse the stator current and extract relevant fault features. A deep belief network (DBN) is utilized for the classification of the health status of the RTMB. To validate the scheme’s feasibility and effectiveness, we conducted experiments on a 1:1 scale high-speed railway traction motor, demonstrating that mechanical defects in RTMB can be reliably indicated by changes in stator current. Based on the analysis of experimental results, it was concluded that the fault detection accuracy of RTMB based on stator current is at least 17.3% higher than that of the fault diagnosis methods based on vibration in diagnosing whether the system has a fault. Among them, the method proposed in this paper is the best in diagnosing the presence and type of faults, with an accuracy that is at least 8.9% higher than other methods. This study not only presents a new method for RTMB monitoring but also contributes to the field by offering a more accurate and efficient alternative to current practices. |
| format | Article |
| id | doaj-art-58ebcd6b6382465b8a2e13403c0fa909 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-58ebcd6b6382465b8a2e13403c0fa9092025-08-20T02:50:37ZengMDPI AGSensors1424-82202024-11-012423767510.3390/s24237675A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator CurrentQi Sun0Juan Zhu1Chunjun Chen2Institute of Applied Electronics, China Academy of Engineering Physics, Mianyang 621900, ChinaPLA Military Space Force, Mianyang 621900, ChinaTechnology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu 610031, ChinaRailway traction motor bearings (RTMB) are critical components in high-speed trains (HST) that are particularly susceptible to failure due to the high stress and rotational frequency they experience. To address the challenge of high false-positive rates in existing monitoring systems, this paper introduces a novel sensorless monitoring scheme that leverages stator current to detect fault-related characteristics, eliminating the need for additional sensors. This approach employs a hybrid signal preprocessing algorithm that integrates adaptive notch filtering (ANF) with envelope spectrum analysis (ESA) to effectively sparse the stator current and extract relevant fault features. A deep belief network (DBN) is utilized for the classification of the health status of the RTMB. To validate the scheme’s feasibility and effectiveness, we conducted experiments on a 1:1 scale high-speed railway traction motor, demonstrating that mechanical defects in RTMB can be reliably indicated by changes in stator current. Based on the analysis of experimental results, it was concluded that the fault detection accuracy of RTMB based on stator current is at least 17.3% higher than that of the fault diagnosis methods based on vibration in diagnosing whether the system has a fault. Among them, the method proposed in this paper is the best in diagnosing the presence and type of faults, with an accuracy that is at least 8.9% higher than other methods. This study not only presents a new method for RTMB monitoring but also contributes to the field by offering a more accurate and efficient alternative to current practices.https://www.mdpi.com/1424-8220/24/23/7675high-speed traintraction motor bearingfault diagnosisstator currentdeep belief network |
| spellingShingle | Qi Sun Juan Zhu Chunjun Chen A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current Sensors high-speed train traction motor bearing fault diagnosis stator current deep belief network |
| title | A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current |
| title_full | A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current |
| title_fullStr | A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current |
| title_full_unstemmed | A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current |
| title_short | A Novel Detection Scheme for Motor Bearing Structure Defects in a High-Speed Train Using Stator Current |
| title_sort | novel detection scheme for motor bearing structure defects in a high speed train using stator current |
| topic | high-speed train traction motor bearing fault diagnosis stator current deep belief network |
| url | https://www.mdpi.com/1424-8220/24/23/7675 |
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