Research on intelligent fault diagnosis for railway point machines using deep reinforcement learning  

作　　者：Shuai Xiao Qingsheng Feng Xue Li Hong Li 

机构地区：[1]School of Automation and Electrical Engineering,Dalian Jiaotong University,Dalian 116028,Liaoning,China [2]School of Rail Transportation,Shandong Jiaotong University,Jinan 250357,Shandong,China [3]School of Software,Dalian Jiaotong University,Dalian 116028,Liaoning,China

出　　处：《Transportation Safety and Environment》2024年第4期75-86,共12页交通安全与环境(英文)

基　　金：supported by the Transportation Science and Technology Project of the Liaoning Provincial Department of Education(Grant No.202243);the Provincial Key Laboratory Project(Grant No.GJZZX2022KF05);the Natural Science Foundation of Liaoning Province(Grant No.2019-ZD-0094).

摘　　要：The advanced diagnosis of faults in railway point machines is crucial for ensuring the smooth operation of the turnout conversion system and the safe functioning of trains.Signal processing and deep learning-based methods have been extensively explored in the realm of fault diagnosis.While these approaches effectively extract fault features and facilitate the creation of end-to-end diagnostic models,they often demand considerable expert experience and manual intervention in feature selection,structural construction and parameter optimization of neural networks.This reliance on manual efforts can result in weak generalization performance and a lack of intelligence in the model.To address these challenges,this study introduces an intelligent fault diagnosis method based on deep reinforcement learning(DRL).Initially,a one-dimensional convolutional neural network agent is established,leveraging the specific characteristics of point machine fault data to automatically extract diverse features across multiple scales.Subsequently,deep Q network is incorporated as the central component of the diagnostic framework.The fault classification interactive environment is meticulously designed,and the agent training network is optimized.Through extensive interaction between the agent and the environment using fault data,satisfactory cumulative rewards and effective fault classification strategies are achieved.Experimental results demonstrate the proposed method’s high efficacy,with a training accuracy of 98.9%and a commendable test accuracy of 98.41%.Notably,the utilization of DRL in addressing the fault diagnosis challenge for railway point machines enhances the intelligence of diagnostic process,particularly through its excellent independent exploration capability.

关 键 词：fault diagnosis railway point machines one-dimensional convolutional neural network deep Q network algorithm 

分 类 号：P31[天文地球—固体地球物理学]