Equipment PHM using non-stationary segmental hidden semi-Markov model

Robotics and Computer-Integrated Manufacturing

Volumn 27, Issue 3, 2011, Pages 581-590

  Dong, Ming ^a   Peng, Ying ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Aging factor; Hazard rate; Non stationary segmental hidden semi Markov model; PHM; Remaining useful life

Indexed keywords

AGING FACTORS; HAZARD RATE; NON-STATIONARY SEGMENTAL HIDDEN SEMI-MARKOV MODEL; PHM; REMAINING USEFUL LIVES;

CONDITION MONITORING; DIAGNOSIS; HAZARDS; HEALTH; HYDRAULIC MACHINERY; IMAGE SEGMENTATION; MAINTAINABILITY; MARKOV PROCESSES; PUMPS;

PROBABILITY DISTRIBUTIONS;

EID: 79551683156     PISSN: 07365845     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rcim.2010.10.005     Document Type: Article

References (20)

1. Kalgren PW, Byington CS, Roemer MJ, Defining PHM, a lexical evolution of maintenance and logistics. In: Proceedings of the 2006 IEEE conference on autotestcon, 1821 September 2006, Anaheim, CA, United States, p. 3538.
2. Millar RC, Defining requirements for advanced PHM technologies for optimal reliability centered maintenance. In: Proceedings of the 2009 IEEE conference on aerospace, 714 March 2009, Big Sky, MT, United States, p. 17.
3. C. Bunks, D. Carthy, and A. Tarik Condition based maintenance of machines using hidden Markov models Mechanical Systems and Signal Processing 14 4 2000 597 612
4. S. Katipamula, and M.R. Brambley Methods for fault detection, diagnostics, and prognostics for building systems-a review Part I HVAC&R Research 11 1 2005 3 25 (Pubitemid 40296371)
5. A.K.S. Jardine, L. Daming, and B. Dragan A review on machinery diagnostics and prognostics implementing condition-based maintenance Mechanical Systems and Signal Processing 20 2006 1483 1510
6. Baruah P, Chinnam RB, HMMs for diagnostics and prognostics in machining processes. In: Proceedings of the 57th society for machine failure prevention technology conference, Virginia Beach, VA, April 2003, p. 148.
7. Zhang XD, Xu R, Chiman K, Liang SY, Xie QL, Haynes L, An integrated approach to bearing fault diagnostics and prognostics. In: Proceedings of the 2005 American control conference, 810 June 2005, Portland, OR, United States, 4, p. 27505.
8. F Camci, Process monitoring, diagnostics and prognostics using support vector machines and hidden Markov models. PhD dissertation, Detroit, Michigan, Graduate school of Wanye State University, 2005.
9. H.Y. Gu, C.Y. Tseng, and L.S. Lee Isolated-utterance speech recognition using hidden Markov models with bounded state duration IEEE Transactions on Singal Processing 39 8 1991 1743 1751
10. A. Falaschi P. Laface, R.De Mori, Continuously Variable Transition Probability HMM for Speech Recognition, in Speech Recognition and Understanding 1992 Springer-Verlag Berlin Heidelberg 125 130
11. M. Dong, and D. He Hidden semi-Markov model based methodology for multi-sensor equipment health diagnosis and prognosis European Journal of Operational Research 178 3 2007 858 878
12. M. Dong, and D. He A segmental hidden semi-Markov model (HSMM) based diagnostics and prognostics framework and methodology Mechanical Systems and Signal Processing 21 5 2007 2248 2266
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 2010 2010 (Article ID 175936, 22 pp.)
14. Z. Ghahramani An introduction to hidden Markov models and Bayesian networks International Journal of Pattern Recognition and Artificial Intelligence 15 1 2001 9 42
15. A. Chen, and G.S. Wu Real-time health prognosis and dynamic preventive maintenance policy for equipment under aging Markovian deterioration International Journal of Production Research 45 15 2007 3351 3379
16. P. Wang, and G. Vachtsevanos Fault prognosis using dynamic wavelet neural networks, artificial intelligence for engineering design Analysis and Manufacturing: AIEDAM 15 4 2001 349 365
17. L. Vctor, M. Riquelme, N. Balakrishnan, and A. Sanhueza Lifetime analysis based on the generalized BirnbaumSaunders distribution Computational Statistics & Data Analysis 52, 2008 2079 2097
18. D. Kumar, and B. Klefsjo Proportional hazards model: a review Reliability Engineering and System Safety 44, 1994 177 188
19. B. Mark, and D.L. Chin Reduction in mean residual life in the presence of a constant competing risk Stochastic Models Business 24, 2008 51 63
20. B. Samanta, and C. Nataraj Prognostics of machine condition using soft computing Robotics and Computer-Integrated Manufacturing 24, 2008 816 823