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Mathematical Problems in Engineering
Volume 2013, Article ID 947104, 9 pages
Research Article

Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference

1State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2National Engineering Research Centre of Rail Transportation Operation and Control Systems, Beijing Jiaotong University, Beijing 100044, China
3Computer Science Department, University of York, York YO10 5GH, UK

Received 27 January 2013; Revised 13 June 2013; Accepted 27 June 2013

Academic Editor: Suiyang Khoo

Copyright © 2013 Tianhua Xu 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.


Risk-based maintenance (RBM) aims to improve maintenance planning and decision making by reducing the probability and consequences of failure of equipment. A new predictive maintenance strategy that integrates dynamic evolution model and risk assessment is proposed which can be used to calculate the optimal maintenance time with minimal cost and safety constraints. The dynamic evolution model provides qualified risks by using probabilistic inference with bucket elimination and gives the prospective degradation trend of a complex system. Based on the degradation trend, an optimal maintenance time can be determined by minimizing the expected maintenance cost per time unit. The effectiveness of the proposed method is validated and demonstrated by a collision accident of high-speed trains with obstacles in the presence of safety and cost constrains.