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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 951584, 22 pages
http://dx.doi.org/10.1155/2012/951584
Research Article

Online Health Management for Complex Nonlinear Systems Based on Hidden Semi-Markov Model Using Sequential Monte Carlo Methods

Department of Operations Management, Antai College of Economics & Management, Shanghai Jiao Tong University, 535 Fahua Zhen Road, Shanghai 200052, China

Received 13 January 2012; Accepted 15 February 2012

Academic Editor: Ming Li

Copyright © 2012 Qinming Liu and Ming Dong. 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. W. Wang, “A two-stage prognosis model in condition based maintenance,” European Journal of Operational Research, vol. 182, no. 3, pp. 1177–1187, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. G. J. Kacprzynski, A. Sarlashkar, M. J. Roemer, A. Hess, and W. Hardman, “Predicting remaining life by fusing the physics of failure modeling with diagnostics,” JOM, vol. 56, no. 3, pp. 29–35, 2004.
  3. C. H. Oppenheimer and K. A. Loparo, “Physically based diagnosis and prognosis of cracked rotor shafts,” in The International Society for Optical Engineering, Components and Systems Diagnostics, Progrnostics, and Health Management II, vol. 4733 of Proceedings of SPIE, pp. 122–132, Orlando, Fla, USA, April 2002. View at Publisher · View at Google Scholar
  4. M. Li, “Fractal time series—a tutorial review,” Mathematical Problems in Engineering, vol. 2010, Article ID 157264, 26 pages, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  5. M. Li and J. Y. Li, “On the predictability of long-range dependent series,” Mathematical Problems in Engineering, vol. 2010, Article ID 397454, 9 pages, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  6. M. Li, C. Cattani, and S. Y. Chen, “Viewing sea level by a one-dimensional random function with long memory,” Mathematical Problems in Engineering, vol. 2011, Article ID 654284, 13 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Li and W. Zhao, “Visiting power laws in cyber-physical networking systems,” Mathematical Problems in Engineering, vol. 2012, Article ID 302786, 13 pages, 2012. View at Publisher · View at Google Scholar
  8. W. S. Chen, P. C. Yuen, and X. Xie, “Kernel machine-based rank-lifting regularized discriminant analysis method for face recognition,” Neurocomputing, vol. 74, no. 17, pp. 2953–2960, 2011. View at Publisher · View at Google Scholar
  9. Z. J. Zhou, C. H. Hu, D. L. Xu, M. Y. Chen, and D. H. Zhou, “A model for real-time failure prognosis based on hidden Markov model and belief rule base,” European Journal of Operational Research, vol. 207, no. 1, pp. 269–283, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  10. C. Burge and S. Karlin, “Prediction of complete gene structures in human genomic DNA,” Journal of Molecular Biology, vol. 268, no. 1, pp. 78–94, 1997. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Deligne and F. Bimbot, “Inference of variable-length acoustic units for continuous speech recognition,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97), pp. 1731–1734, April 1997. View at Scopus
  12. W. H. Majoros, M. Pertea, A. L. Delcher, and S. L. Salzberg, “Efficient decoding algorithms for generalized hidden Markov model gene finders,” BMC Bioinformatics, vol. 6, article no. 16, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Dong, “A tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction: Concepts, models, and algorithms,” Mathematical Problems in Engineering, vol. 2010, Article ID 175936, 22 pages, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. M. Dong and D. He, “Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis,” European Journal of Operational Research, vol. 178, no. 3, pp. 858–878, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  15. M. Dong and D. He, “A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology,” Mechanical Systems and Signal Processing, vol. 21, no. 5, pp. 2248–2266, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. L. R. Rabiner, “Tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, 1989. View at Publisher · View at Google Scholar · View at Scopus
  17. Z. H. Tang and R. Q. Wang, “The research of definition and feature on Dirac delta function,” Journal of Liuzhou Vocational & Technical College, vol. 9, no. 2, pp. 68–76, 2009.
  18. A. Doucet, S. Godsill, and C. Andrieu, “On sequential Monte Carlo sampling methods for Bayesian filtering,” Statistics and Computing, vol. 10, no. 3, pp. 197–208, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Kong, J. S. Liu, and W. H. Wong, “Sequential imputations and Bayesian missing data problems,” Journal of the American Statistical Association, vol. 89, no. 425, pp. 278–288, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  20. T. Furukawa, “SOM of SOMs,” Neural Networks, vol. 22, no. 4, pp. 463–478, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. K. M. Hancock and Q. Zhang, “A hybrid approach to hydraulic vane pump condition monitoring and fault detection,” Transactions of the ASABE, vol. 49, no. 4, pp. 1203–1211, 2006. View at Scopus