Reliability modeling and analysis of on-board subsystem of high-speed railway ATO system based on Bayesian network

2021 ◽  
Author(s):  
Yuerong Wang ◽  
Zhenhai Zhang
Keyword(s):  
Bayesian Network ◽  
High Speed ◽  
High Speed Railway ◽  
Reliability Modeling ◽  
Modeling And Analysis

scholarly journals Bayesian Network Based Fault Prognosis via Bond Graph Modeling of High-Speed Railway Traction Device

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yunkai Wu ◽  
Bin Jiang ◽  
Ningyun Lu ◽  
Yang Zhou
Keyword(s):  
Bayesian Network ◽  
High Speed ◽  
Bond Graph ◽  
Fault Propagation ◽  
High Speed Railway ◽  
Graph Modeling ◽  
Railway Traction ◽  
Bond Graph Modeling ◽  
Fault Prognosis ◽  
Traction System

Reliability of the traction system is of critical importance to the safety of CRH (China Railway High-speed) high-speed train. To investigate fault propagation mechanism and predict the probabilities of component-level faults accurately for a high-speed railway traction system, a fault prognosis approach via Bayesian network and bond graph modeling techniques is proposed. The inherent structure of a railway traction system is represented by bond graph model, based on which a multilayer Bayesian network is developed for fault propagation analysis and fault prediction. For complete and incomplete data sets, two different parameter learning algorithms such as Bayesian estimation and expectation maximization (EM) algorithm are adopted to determine the conditional probability table of the Bayesian network. The proposed prognosis approach using Pearl’s polytree propagation algorithm for joint probability reasoning can predict the failure probabilities of leaf nodes based on the current status of root nodes. Verification results in a high-speed railway traction simulation system can demonstrate the effectiveness of the proposed approach.

A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries

2020 ◽  
Vol 21 (10) ◽  
pp. 4037-4051 ◽  
Author(s):  
Hongrui Wang ◽  
Alfredo Nunez ◽  
Zhigang Liu ◽  
Dongliang Zhang ◽  
Rolf Dollevoet
Keyword(s):  
Bayesian Network ◽  
Condition Monitoring ◽  
High Speed ◽  
Network Approach ◽  
High Speed Railway
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