Fault Diagnosis of Subway Vehicle Traction System Based on Bayesian Network
CHEN Yafeng
WEN Jing
ZHOU Feng
XIAO Huijie
CHAI Xiaodong
ZHENG Shubin
Abstract：Based on the historical fault data of the Shanghai metro vehicle traction system,a vehicle component fault diagno-sis method is proposed.The fault features in the fault data are extracted using a structural topic model,the number of features is searched for,and document covariates are introduced to improve the criticality of the fault features.The uncertainty between the fault features and the cause of the faults is solved using Bayesian networks,and the fault diagnosis model is improved by combining expert knowledge and fault data and optimizing the Bayesian network structure through a dynamic programming-Markov Monte Car-lo joint structure learning algorithm accuracy.The results show that the established fault diagnosis model has high diagnostic accura-cy and superiority,and the diagnostic results can provide a reference for the rapid diagnosis of subway vehicle traction system faults.
Keywords：rail transit vehiclestraction systemfault diagnosisstructural topic modelBayesian networks
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 660-665 )