铁道科学与工程学报2025，Vol.22Issue(5)：2380-2392,13.DOI:10.19713/j.cnki.43-1423/u.T20241273
基于CNN-LSTM-Attention的ZPW-2000A轨道电路故障诊断方法
Fault diagnosis of ZPW-2000A track circuit based on CNN-LSTM-Attention
摘要
Abstract
Track circuit is one of the important railways signaling equipment,which will directly affect the safety of train operation in case of failure.Traditional fault diagnosis methods have limitations in extracting global and local features of faults,and the rise of deep learning methods provides new solutions to solve this problem.This paper proposed a fault diagnosis method based on CNN,LSTM and Attention mechanism,denoted as CNN-LSTM-Attention,with ZPW-2000A as the research object.Specifically,the method extracted local features of track circuit faults by CNN,mined correlation and global features of time series data by LSTM,and then introduced Attention mechanism to assign different weights to the features,and finally realized fault diagnosis.Finally,this paper collected 31 common faults of ZPW-2000A rail circuit,simulates the fault curves and generates a data set.Experimental validation was carried out on this data set.The results show that compared with mainstream methods,CNN-LSTM-Attention has the best diagnostic performance and strong generalization ability.In conclusion,the method proposed in the paper can provide important theoretical and technical support for track circuit fault diagnosis and safe operation of railroad systems.关键词
ZPW-2000A/故障诊断/深度学习/CNN/LSTM/注意力机制Key words
ZPW-2000A/fault diagnosis/deep learning/CNN/LSTM/attention mechanisms分类
交通工程引用本文复制引用
杨勇,可婷,胡启正,张志敏..基于CNN-LSTM-Attention的ZPW-2000A轨道电路故障诊断方法[J].铁道科学与工程学报,2025,22(5):2380-2392,13.基金项目
中国国家铁路集团有限公司科研项目(L2022G004) （L2022G004）
