现代电子技术2025，Vol.48Issue(4)：57-62,6.DOI:10.16652/j.issn.1004-373x.2025.04.010
基于时序数据的列车牵引系统故障预测方法
Method of train traction system fault prediction based on timeseries data
摘要
Abstract
As a key module for the conversion of train kinetic energy,traction system will bring great safety risks to the normal operation of the vehicle if it fails,so it is of great significance to predict its failure.However,traditional prediction methods have problems such as high dependence on manual experience judgment,inability to include a large number of fault features,and insufficient prediction accuracy.On this basis,a method of fault prediction based on timeseries data is proposed.The XGBoost algorithm is used to calculate and screen the fault features of the train traction converter system to determine the key features that are strongly correlated with the converter faults.The LSTM model optimized by Bayes is used to adaptively learn the multi-source variable data features,and the time window is used to intercept the feature variable data to realize the prediction of different types of faults.The experimental results show that The accuracy of the proposed method can reach more than 91%when predicting 6 kinds of faults in converter scenario.关键词
牵引系统/故障预测/时序数据/XGBoost算法/LSTM/时间窗Key words
traction system/fault prediction/timeseries data/XGBoost algorithm/LSTM/time window分类
信息技术与安全科学引用本文复制引用
贺鑫来,孙庚,汪敏捷,翟逸男,陈岩霖,尹娴,冯艳红..基于时序数据的列车牵引系统故障预测方法[J].现代电子技术,2025,48(4):57-62,6.基金项目
大连海洋大学科研项目:轨道列车智能运维管理平台(2023001) （2023001）
