Electrical Grounding Fault Prediction of EMU Traction Motor - Xuemiao PANG; Chunxing PEI; Chunguang YAN; Dongxing WANG; Jie JIANG (机车电传动)
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.