Remaining useful life estimation based on stochastic deterioration models: A comparative study
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Benoît Iung | Mitra Fouladirad | Anne Barros | Khanh Le Son | Eric Levrat | A. Barros | B. Iung | E. Levrat | M. Fouladirad | K. Son
[1] P. Salminen. On the first hitting time and the last exit time for a Brownian motion to/from a moving boundary , 1988, Advances in Applied Probability.
[2] Abhinav Saxena,et al. Damage propagation modeling for aircraft engine run-to-failure simulation , 2008, 2008 International Conference on Prognostics and Health Management.
[3] Nancy J. Lybeck,et al. Prognostics and life beyond 60 years for nuclear power plants , 2011, 2011 IEEE Conference on Prognostics and Health Management.
[4] Wenbin Wang,et al. A model for residual life prediction based on Brownian motion with an adaptive drift , 2011, Microelectron. Reliab..
[5] L.J. Bond,et al. Proactive Management of Materials Degradation for nuclear power plant systems , 2008, 2008 International Conference on Prognostics and Health Management.
[6] J.J. Palazzolo,et al. Leakage Fault Detection Method for Axial-Piston Variable Displacement Pumps , 2008, 2008 IEEE Aerospace Conference.
[7] Donghua Zhou,et al. Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process , 2012, IEEE Transactions on Reliability.
[8] L. Peel,et al. Data driven prognostics using a Kalman filter ensemble of neural network models , 2008, 2008 International Conference on Prognostics and Health Management.
[9] J.W. Hines,et al. Prognostic algorithm categorization with PHM Challenge application , 2008, 2008 International Conference on Prognostics and Health Management.
[10] G A Whitmore,et al. Modelling Accelerated Degradation Data Using Wiener Diffusion With A Time Scale Transformation , 1997, Lifetime data analysis.
[11] F. J. Schuurmann,et al. Evaluations of barrier-crossing probabilities of Wiener paths , 1976, Journal of Applied Probability.
[12] D. Cox,et al. Analysis of Survival Data. , 1985 .
[13] Robert Fildes,et al. The evaluation of extrapolative forecasting methods , 1992 .
[14] Benoît Iung,et al. Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system , 2008, Reliab. Eng. Syst. Saf..
[15] G A Whitmore,et al. Estimating degradation by a wiener diffusion process subject to measurement error , 1995, Lifetime data analysis.
[16] W. J. Padgett,et al. Inference from Accelerated Degradation and Failure Data Based on Gaussian Process Models , 2004, Lifetime data analysis.
[17] M. Yor,et al. Continuous martingales and Brownian motion , 1990 .
[18] Wenbin Wang. A two-stage prognosis model in condition based maintenance , 2007, Eur. J. Oper. Res..
[19] Masoud Rabiei,et al. A probabilistic-based airframe integrity management model , 2009, Reliab. Eng. Syst. Saf..
[20] Jay Lee,et al. Similarity based method for manufacturing process performance prediction and diagnosis , 2007, Comput. Ind..
[21] Carl S. Byington,et al. Electronic prognostics - a case study using Global Positioning System (GPS) , 2005, IEEE Autotestcon, 2005..
[22] Sheng-Tsaing Tseng,et al. Stochastic Diffusion Modeling of Degradation Data , 2007, Journal of Data Science.
[23] Cleve B. Moler,et al. Numerical computing with MATLAB , 2004 .
[24] Chao Hu,et al. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life , 2011, 2011 IEEE Conference on Prognostics and Health Management.
[25] Piero Baraldi,et al. Ensemble of bootstrapped models for the prediction of the remaining useful life of a creeping turbine blade , 2012, 2012 IEEE Conference on Prognostics and Health Management.
[26] Xiao Wang,et al. Wiener processes with random effects for degradation data , 2010, J. Multivar. Anal..
[27] H. Kaebernick,et al. Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks , 2007 .
[28] F.O. Heimes,et al. Recurrent neural networks for remaining useful life estimation , 2008, 2008 International Conference on Prognostics and Health Management.
[29] K. Doksum,et al. Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse Gaussian distribution , 1992 .
[30] I. Jolliffe. Principal Component Analysis , 2002 .
[31] J. Scott Armstrong,et al. Evaluation of Extrapolative Forecasting Methods: Results of a Survey of Academicians and Practitioners , 1982 .
[32] Enrico Zio,et al. A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system , 2010, Reliab. Eng. Syst. Saf..
[33] Dragan Banjevic,et al. Remaining useful life in theory and practice , 2009 .
[34] Byeng D. Youn,et al. A generic probabilistic framework for structural health prognostics and uncertainty management , 2012 .
[35] Donghua Zhou,et al. Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..
[36] J. L. Folks,et al. The Inverse Gaussian Distribution as a Lifetime Model , 1977 .
[37] Jianbo Yu,et al. A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems , 2008, 2008 International Conference on Prognostics and Health Management.
[38] E. Bechhoefer,et al. Development and Validation of Bearing Diagnostic and Prognostic Tools using HUMS Condition Indicators , 2008, 2008 IEEE Aerospace Conference.
[39] Carl S. Byington,et al. Prognostic Enhancements to Diagnostic Systems (PEDS) Applied to Shipboard Power Generation Systems , 2004 .
[40] Enrico Zio,et al. Particle filtering prognostic estimation of the remaining useful life of nonlinear components , 2011, Reliab. Eng. Syst. Saf..
[41] Rob J Hyndman,et al. Another look at measures of forecast accuracy , 2006 .