Electrical Grounding Fault Prediction of EMU Traction Motor

Xuemiao PANG; Chunxing PEI; Chunguang YAN; Dongxing WANG; Jie JIANG

doi:10.13890/j.issn.1000-128x.2021.04.020

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Electrical Grounding Fault Prediction of EMU Traction Motor

Intelligent Technology | 更新时间：2021-12-09

- Electrical Grounding Fault Prediction of EMU Traction Motor
- Electric Drive for Locomotives Issue 4, Pages: 126-130(2021)
- 作者机构：
  
  1.中车唐山机车车辆有限公司，河北　唐山　063035
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128x.2021.04.020
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Xuemiao PANG, Chunxing PEI, Chunguang YAN, et al. Electrical Grounding Fault Prediction of EMU Traction Motor. [J]. Electric Drive for Locomotives (4):126-130(2021)
DOI：

Xuemiao PANG, Chunxing PEI, Chunguang YAN, et al. Electrical Grounding Fault Prediction of EMU Traction Motor. [J]. Electric Drive for Locomotives (4):126-130(2021) DOI： 10.13890/j.issn.1000-128x.2021.04.020.

摘要

牵引电机是动车组动力传动系统中的关键部件，在牵引电机故障中最常见的故障为牵引电机接地故障。通过对某动车组牵引电机控制单元的历史数据挖掘，实现对牵引电机接地故障的预测。数据挖掘建模使用了RBF神经网络、决策树和支持向量机3种机器学习算法。试验结果表明，3种算法的预测准确度均高于84%，其中决策树相较于RBF神经网络和支持向量机，具有更高的预测精度，模型预测精度达到85.6%。因此，选取决策树模型预测动车组牵引电机接地故障的发生。

Abstract

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.

关键词

动车组牵引电机高速铁路接地故障预测决策树RBF神经网络支持向量机故障诊断

Keywords

EMUtraction motorhigh-speed railwaygrounding fault predictiondecision treeRBF neural networkSVMfault diagnosis

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