Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network

Abstract

Train network control management system (TCMS) is an important part of the High-speed rail train. Because of the TCMS's complex and redundant structure, long-term operation environment, etc., breakdowns inevitably in the long-time running. Based on the historical fault data of the TCMS accumulated during their online service, the working principles, failure modes, and effects analysis of TCMS are researched and the dynamic fault tree (DFT) model of TCMS failure is built. Then, the dynamic fault tree model is transformed into the Bayesian network (BN) model, which can model the reliability of such types of systems. Finally, combining DFT with BN is used for fault probability estimation and reliability assessment. The results present that increasing the reliability of key modules for the TCMS would be of great help to High-speed rail train engineers in the fault diagnosis field.