Condition monitoring and remaining useful life prediction using degradation signals: revisited

References

• Bae , S. J. and Kvam , P. H. 2006 . A change-point analysis for modeling incomplete burn-in for light displays . IIE Transactions , 38 : 489 – 498 .
   Web of Science ®Google Scholar
• Baruah , P. and Chinnam , R. B. 2005 . HMMs for diagnostics and prognostics in machining processes . International Journal of Production Research , 43 : 1275 – 1293 .
   Web of Science ®Google Scholar
• Carlin , B.P. and Louis , T.A. 2000 . Bayes and Empirical Bayes Methods for Data Analysis , New York , NY : Chapman & Hall/CRC .
   Google Scholar
• Chakraborty , S. , Gebraeel , N. , Lawley , M. and Wan , H. 2009 . Residual-life estimation for components with non-symmetric priors . IIE Transactions , 41 : 372 – 387 .
   Web of Science ®Google Scholar
• Chen , N. , Chen , Y. , Li , Z. , Zhou , S. and Sievenpiper , C. 2011 . Optimal variability sensitive condition-based maintenance with a Cox PH model . International Journal of Production Research , 49 : 2083 – 2100 .
   Web of Science ®Google Scholar
• De Boor , C. 2001 . A Practical Guide to Splines , New York , NY : Springer Verlag .
   Google Scholar
• Doksum , K. A. and Hyland , A. 1992 . Models for variable-stress accelerated life testing experiments based on wiener processes and the inverse Gaussian distribution . Technometrics , 34 : 74 – 82 .
   Web of Science ®Google Scholar
• Gebraeel , N. 2006 . Sensory-updated residual life distributions for components with exponential degradation patterns . IEEE Transactions on Automation Science and Engineering , 3 : 382 – 393 .
   Web of Science ®Google Scholar
• Gebraeel , N. , Lawley , M. , Li , R. and Ryan , J. 2005 . Residual-life distributions from component degradation signals: a Bayesian approach . IIE Transactions , 37 : 543 – 557 .
   Web of Science ®Google Scholar
• Gebraeel , N. Z. and Lawley , M. A. 2008 . A neural network degradation model for computing and updating residual life distributions . IEEE Transactions on Automation Science and Engineering , 5 : 154 – 163 .
   Web of Science ®Google Scholar
• Gelman , A. , Carlin , J.B. , Stern , H.S. and Rubin , D.B. 2004 . Bayesian Data Analysis, second edition , New York , NY : Chapman & Hall/CRC .
   Google Scholar
• Kumar , D. and Klefsj , B. 1994 . Proportional hazards model: a review . Reliability Engineering & System Safety , 44 : 177 – 188 .
   Web of Science ®Google Scholar
• Lai , T. L. 1995 . Sequential changepoint detection in quality control and dynamical systems . Journal of the Royal Statistical Society. Series B(Methodological) , 57 : 613 – 658 .
   Google Scholar
• Lin , J. 2008 . A two-stage failure model for Bayesian change point analysis . IEEE Transactions on Reliability , 57 : 388 – 393 .
   Web of Science ®Google Scholar
• Loader , C. R. 1991 . Inference for a hazard rate change point . Biometrika , 78 : 749 – 757 .
   Web of Science ®Google Scholar
• Lu , C.J. and Meeker , W.Q. 1993 . Using degradation measures to estimate a time-to-failure distribution , Vol. 35 , 161 – 174 . Technometrics .
   Google Scholar
• Nelson , W.B. 1990 . Accelerated Testing: Statistical Models, Test Plans, and Data Analysis , New York , NY : John Wiley & Sons .
   Google Scholar
• Ng , T. S. 2008 . An application of the EM algorithm to degradation modeling . IEEE Transactions on Reliability , 57 : 2 – 13 .
   Web of Science ®Google Scholar
• Park , C. and Padgett , W. 2005 . Accelerated degradation models for failure based on geometric brownian motion and gamma processes . Lifetime Data Analysis , 11 : 511 – 527 .
   PubMed Web of Science ®Google Scholar
• Pecht , M.G. 2008 . Prognostics and Health Management of Electronics , Hoboken , NJ : John Wiley & Sons .
   Google Scholar
• Seber , G.A.F. and Lee , A.J. 2003 . Linear Regression Analysis , Hoboken , NJ : John Wiley & Sons .
   Google Scholar
• Si , X.-S. , Wang , W. , Hu , C.-H. and Zhou , D.-H. 2011 . Remaining useful life estimation—a review on the statistical data driven approaches . European Journal of Operational Research , 213 : 1 – 14 .
   Web of Science ®Google Scholar
• Si , X. , Wang , W. , Hu , C. , Zhou , D. and Pecht , M. 2012 . Remaining useful life estimation based on a nonlinear diffusion degradation process . IEEE Transactions on Reliability , 61 : 50 – 67 .
   Web of Science ®Google Scholar
• Siegmund , D. and Venkatraman , E. S. 1995 . Using the generalized likelihood ratio statistic for sequential detection of a change-point . The Annals of Statistics , 23 : 255 – 271 .
   Web of Science ®Google Scholar
• Wang , W. 2007 . A two-stage prognosis model in condition based maintenance . European Journal of Operational Research , 182 : 1177 – 1187 .
   Web of Science ®Google Scholar
• Wang , W. , Hussin , B. and Jefferis , T. 2012 . A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering . International Journal of Production Economics , 136 : 84 – 92 .
   Web of Science ®Google Scholar
• Ye , Z. , Shen , Y. and Xie , M. 2012 . Degradation-based burn-in with preventive maintenance . European Journal of Operational Research , 221 : 360 – 367 .
   Web of Science ®Google Scholar
• Ye , Z. , Xie , M. , Tang , L. and Shen , Y. 2012 . Degradation-based burn-in planning under competing risks . Technometrics , 54 : 159 – 168 .
   Web of Science ®Google Scholar
• Ye , Z.S. , Xie , M. , Tang , L.C. and Chen , N. 2012 . Efficient semiparametric estimation of gamma processes for deteriorating products , Technometrics . in press
   Google Scholar
• Yuan , T. and Kuo , Y. 2010 . Bayesian analysis of hazard rate, change point, and cost-optimal burn-in time for electronic devices . IEEE Transactions on Reliability , 59 : 132 – 138 .
   Web of Science ®Google Scholar