Electrical Grounding Fault Prediction of EMU Traction Motor
The traction motor plays a key role in the EMU train’s power transmission system. The most frequent fault of traction motor is grounding fault. By using RBF neural network, decision tree and support vector machine (SVM) respectively, the traction motor’s electrical fault prediction model was built b...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Drive for Locomotives
2021-07-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2021.04.020 |
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| author | Xuemiao PANG Chunxing PEI Chunguang YAN Dongxing WANG Jie JIANG |
| author_facet | Xuemiao PANG Chunxing PEI Chunguang YAN Dongxing WANG Jie JIANG |
| author_sort | Xuemiao PANG |
| collection | DOAJ |
| description | The traction motor plays a key role in the EMU train’s power transmission system. The most frequent fault of traction motor is grounding fault. By using RBF neural network, decision tree and support vector machine (SVM) respectively, the traction motor’s electrical fault prediction model was built based on the historical data of the traction motor control unit. It shows that the prediction accuracy of the three algorithms is higher than 84% at all. And comparing with RBF neural network and support vector machine, decision tree has higher prediction accuracy and reaches 85.6%. Therefore, the decision tree was selected to predict the occurrence of grounding fault. |
| format | Article |
| id | doaj-art-e05094d6b76242b99a55fc340331c5b0 |
| institution | DOAJ |
| issn | 1000-128X |
| language | zho |
| publishDate | 2021-07-01 |
| publisher | Editorial Department of Electric Drive for Locomotives |
| record_format | Article |
| series | 机车电传动 |
| spelling | doaj-art-e05094d6b76242b99a55fc340331c5b02025-08-20T03:09:13ZzhoEditorial Department of Electric Drive for Locomotives机车电传动1000-128X2021-07-0112613020903958Electrical Grounding Fault Prediction of EMU Traction MotorXuemiao PANGChunxing PEIChunguang YANDongxing WANGJie JIANGThe traction motor plays a key role in the EMU train’s power transmission system. The most frequent fault of traction motor is grounding fault. By using RBF neural network, decision tree and support vector machine (SVM) respectively, the traction motor’s electrical fault prediction model was built based on the historical data of the traction motor control unit. It shows that the prediction accuracy of the three algorithms is higher than 84% at all. And comparing with RBF neural network and support vector machine, decision tree has higher prediction accuracy and reaches 85.6%. Therefore, the decision tree was selected to predict the occurrence of grounding fault.http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2021.04.020EMUtraction motorhigh-speed railwaygrounding fault predictiondecision treeRBF neural networkSVMfault diagnosis |
| spellingShingle | Xuemiao PANG Chunxing PEI Chunguang YAN Dongxing WANG Jie JIANG Electrical Grounding Fault Prediction of EMU Traction Motor 机车电传动 EMU traction motor high-speed railway grounding fault prediction decision tree RBF neural network SVM fault diagnosis |
| title | Electrical Grounding Fault Prediction of EMU Traction Motor |
| title_full | Electrical Grounding Fault Prediction of EMU Traction Motor |
| title_fullStr | Electrical Grounding Fault Prediction of EMU Traction Motor |
| title_full_unstemmed | Electrical Grounding Fault Prediction of EMU Traction Motor |
| title_short | Electrical Grounding Fault Prediction of EMU Traction Motor |
| title_sort | electrical grounding fault prediction of emu traction motor |
| topic | EMU traction motor high-speed railway grounding fault prediction decision tree RBF neural network SVM fault diagnosis |
| url | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2021.04.020 |
| work_keys_str_mv | AT xuemiaopang electricalgroundingfaultpredictionofemutractionmotor AT chunxingpei electricalgroundingfaultpredictionofemutractionmotor AT chunguangyan electricalgroundingfaultpredictionofemutractionmotor AT dongxingwang electricalgroundingfaultpredictionofemutractionmotor AT jiejiang electricalgroundingfaultpredictionofemutractionmotor |