Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 947104, 9 pages
http://dx.doi.org/10.1155/2013/947104
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.

Linked References

  1. J. Lee, J. Ni, D. Djurdjanovic, H. Qiu, and H. Liao, “Intelligent prognostics tools and e-maintenance,” Computers in Industry, vol. 57, no. 6, pp. 476–489, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Hu, L. Zhang, and W. Liang, “Opportunistic predictive maintenance for complex multi-component systems based on DBN-HAZOP model,” Process Safety and Environmental Protection, vol. 90, pp. 376–388, 2012. View at Publisher · View at Google Scholar
  3. L. Krishnasamy, F. Khan, and M. Haddara, “Development of a risk-based maintenance (RBM) strategy for a power-generating plant,” Journal of Loss Prevention in the Process Industries, vol. 18, no. 2, pp. 69–81, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. N. S. Arunraj and J. Maiti, “Risk-based maintenance-techniques and applications,” Journal of Hazardous Materials, vol. 142, no. 3, pp. 653–661, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. F. V. Jensen, S. L. Lauritzen, and K. G. Olesen, “Bayesian updating in causal probablisitic networks by local computations,” Computational Statistics Quarterly, vol. 4, pp. 269–292, 1990. View at Google Scholar
  6. A. P. Dawid, “Applications of a general propagation algorithm for probabilistic expert systems,” Statistics and Computing, vol. 2, no. 1, pp. 25–36, 1992. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Draper, “Clustering without (thinking about) triangulation,” in Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 1995.
  8. X. An, Y. Xiang, and N. Cercone, “Dynamic multiagent probabilistic inference,” International Journal of Approximate Reasoning, vol. 48, no. 1, pp. 185–213, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Donat, P. Leray, L. Bouillaut, and P. Aknin, “A dynamic Bayesian network to represent discrete duration models,” Neurocomputing, vol. 73, no. 4-6, pp. 570–577, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Dechter, “Bucket elimination: a unifying framework for reasoning,” Artificial Intelligence, vol. 113, no. 1, pp. 41–85, 1999. View at Publisher · View at Google Scholar · View at Scopus