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Mathematical Problems in Engineering
Volume 2015, Article ID 321872, 11 pages
Research Article

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

1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2Jiangsu Key Laboratory of Internet of Things and Control Technologies, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Received 5 September 2014; Accepted 25 December 2014

Academic Editor: Peng Shi

Copyright © 2015 Yunkai Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.