Stochastic methodology for prognostics under continuously varying environmental profiles

Statistical Analysis and Data Mining

Volumn 6, Issue 3, 2013, Pages 260-270

  Bian, Linkan ^a   Gebraeel, Nagi ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

Degradation; Prognostics; Real time updating; Stochastic model

Indexed keywords

STOCHASTIC SYSTEMS; SYSTEMS ENGINEERING;

DEGRADATION SIGNALS; ENVIRONMENTAL PROFILE; MODELLING FRAMEWORK; PROGNOSTIC; REAL- TIME; REAL-TIME UPDATING; RESIDUAL LIFE DISTRIBUTION; RESIDUAL LIFETIME; STOCHASTIC METHODOLOGY; STOCHASTIC-MODELING;

STOCHASTIC MODELS;

EID: 84877904648     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.11154     Document Type: Article

References (40)

1. A. K. S. Jardine, D. Banjevic, and V. Makis, Optimal replacement policy and the structure of software for condition-based maintenance, J Qual Mainten Eng 3(2) (1997), 109-119.
2. M. L. T. Lee, G. A. Whitmore, F. Laden, J. E. Hart, and E. Garshick, Assessing lung cancer risk in railroad workers using a first hitting time regression model, Environmetrics 15(5) (2004), 501-512.
3. K. A. Doksum and A. Hóyland, Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse Gaussian distribution, Technometrics 34(1) (1992), 74-82.
4. J. P. Kharoufeh, Explicit results for wear processes in a Markovian environment, Oper Res Lett 31(3) (2003), 237-244.
5. N. D. Singpurwalla, Survival in dynamic environments, Stat Sci 10(1) (1995), 86-103.
6. J. M. van Noortwijk, A survey of the application of gamma processes in maintenance, Reliability Eng Syst Saf 94(1) (2009), 2-21.
7. C. J. Lu and W. Q. Meeker, Using degradation measures to estimate a time-to-failure distribution, Technometrics 35(2) (1993), 161-174.
8. L. I. Pettit and K. D. S. Young, Bayesian analysis for inverse Gaussian lifetime data with measures of degradation, J Stat Comput Simul 63(3) (1999), 217-234.
9. C. Park and W. J. Padgett, New cumulative damage models for failure using stochastic processes as initial damage, IEEE Trans Reliability 54(3) (2005), 530-540.
10. N. Gebraeel, M. A. Lawley, R. Li, and J. K. Ryan, Residual-life distributions from component degradation signals: A Bayesian approach, IIE Trans 37(6) (2005), 543-557.
11. M. E. Robinson and M. J. Crowder, Bayesian methods for a growth-curve degradation model with repeated measures, Lifetime Data Anal 6(4) (2000), 357-374.
12. D. R. Cox and H. D. Miller, The Theory of Stochastic Processes. New York, Chapman and Hall, 1995.
13. N. Gebraeel, Sensory-updated residual life distributions for components with exponential degradation patterns, IEEE Trans Autom Sci Eng 3(4) (2006), 382-393.
14. X. Wang, Wiener processes with random effects for degradation data, J Multivariate Anal 101(2) (2010), 340-351.
15. J. Lawless and M. Crowder, Covariates and random effects in a gamma process model with application to degradation and failure, Lifetime Data Anal 10(3) (2004), 213-227.
16. C. Park and W. J. Padgett, Accelerated degradation models for failure based on geometric Brownian motion and gamma processes, Lifetime Data Anal 11(4) (2005), 511-527.
17. C. Park and W. J. Padgett, Stochastic degradation models with several accelerating variables, IEEE Trans Reliability 55(2) (2006), 379-390.
18. S. T. Tseng, N. Balakrishnan, and C. C. Tsai, Optimal step-stress accelerated degradation test plan for gamma degradation processes, IEEE Trans Reliability 58(4) (2009), 611-618.
19. N. Ebrahimi, The mean function of a repairable system that is subjected to an imperfect repair policy, IIE Trans 41(1) (2009), 57-64.
20. D. R. Cox, Regression models and life-tables, J R Stat Soc [Ser B] 34(2) (1972), 187-220.
21. D. Banjevic, A. K. S. Jardine, V. Makis, and M. Ennis, A control-limit policy and software for condition-based maintenance optimization, INFOR-OTTAWA 39(1) (2001), 32-50.
22. C. M. Liao, and S. T. Tseng, Optimal design for step-stress accelerated degradation tests, IEEE Trans Reliability 55(1) (2006), 59-66.
23. N. Gebraeel and J. Pan, Prognostic degradation models for computing and updating residual life distributions in a time-varying environment, IEEE Trans Reliability 57(4) (2008), 539-550.
24. X. S. Si, W. Wang, C. H. Hu, and D. H. Zhou, Remaining useful life estimation-a review on the statistical data driven approaches, Eur J Oper Res 213(1) (2010), 1-14.
25. L. E. Myers, Survival functions induced by stochastic covariate processes, J Appl Probab 18(2) (1981), 523-529.
26. D. Gamerman, Dynamic Bayesian models for survival data, Appl Stat 40(1) (1991), 63-79.
27. M. Pijnenburg, Additive hazards models in repairable systems reliability, Reliability Eng Syst Saf 31(3) (1991), 369-390.
28. D. Banjevic and A. K. S. Jardine, Calculation of reliability function and remaining useful life for a Markov failure time process, IMA J Manage Math 17(2) (2006), 115.
29. A. Ghasemi, S. Yacout, and M. S. Ouali, Evaluating the reliability function and the mean residual life for equipment with unobservable states, IEEE Trans Reliability 59(1) (2010), 45-54.
30. X. Zhao, M. Fouladirad, C. Bérenguer, and L. Bordes, Condition-based inspection/replacement policies for non-monotone deteriorating systems with environmental covariates, Reliability Eng Syst Saf 95(8) (2010), 921-934.
31. P. J. Vlok, M. Wnek, and M. Zygmunt, Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions, Mech Syst Signal Process 18(4) (2004), 833-847.
32. Y. Sun, L. Ma, J. Mathew, W. Wang, and S. Zhang, Mechanical systems hazard estimation using condition monitoring, Mech Syst Signal Process 20(5) (2006), 1189-1201.
33. H. Liao, W. Zhao, and H. Guo, Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model, In Reliability and Maintainability Symposium, 2006. RAMS'06. Annual, IEEE, 2006, 127-132.
34. G. A. Whitmore and F. Schenkelberg, Modelling accelerated degradation data using wiener diffusion with a time scale transformation, Lifetime Data Anal 3(1) (1997), 27-45.
35. J. P. Kharoufeh, D. E. Finkelstein, and D. G. Mixon, Availability of periodically inspected systems with Markovian wear and shocks, J Appl Probab 43(2) (2006), 303-317.
36. H. Liao and E. A. Elsayed, Reliability inference for field conditions from accelerated degradation testing, Nav Res Logist 53(6) (2006), 576-587.
37. Mei-Ling Ting Lee and G. A. Whitmore, Proportional hazards and threshold regression: their theoretical and practical connections, Lifetime Data Anal (Sp. Iss. SI) 16(2) (2010), 196-214.
38. H. E. Daniels, Approximating the first crossing-time density for a curved boundary, Bernoulli 2(2) (1996), 133-143.
39. T. A. Harris and M. N. Kotzalas, Rolling Bearing Analysis. New York, Wiley, 1984.
40. W. Nelson, Accelerated Testing: Statistical Models, Test Plans and Data Analyses. Hoboken, NJ, John Wiley & Sons Inc., 2008.