[2] VICHARE N M, PECHT M G. Prognostics and health ma-nagement of electronics［M］. New Jersey: John Wiley & Sons, 2008.
[3] SI X S, WANG W B, HU C H, et al. Remaining useful life estimation—a review on the statistical data driven appro-aches［J］. European Journal of Operational Research, 2011, 213 (1): 1-14.
[4] KALBFLEISCH J D, PRENTICE R L. The statistical ana-lysis of failure time data［M］. New Jersey: John Wiley & Sons, 2002.
[5] GERTSBACKH B I B, KORDONSKIY K B. Models of fai-lure［M］. New York: Springer Berlin Heidelberg, 1969.
[6] LU C J, MEEKER W Q. Using degradation measures to estimate a time-to-failure distribution［J］. Technome-trics, 1993, 35(2): 161-174.
[7] PENG C Y, TSENG S T. Statistical lifetime inference with skew-Wiener linear degradation models［J］. IEEE Tran-sactions on Reliability, 2013, 62(2): 338-350.
[8] COX D R. Some remarks on failure-times, surrogate mar-kers, degradation, wear, and the quality of life［J］. Lifetime Data Anal, 1999, 5(4): 307-314.
[9] BAGDONAVICIUS V, BIKELIS A, KAZAKEVICIUS V, et al. Analysis of joint multiple failure mode and linear degradation data with renewals［J］. Journal of Statistical Planning and Inference, 2007, 137(7): 2191-2207.
[13] SI X S, WANG W B, HU C H, et al. Remaining useful life estimation based on a nonlinear diffusion degradation process［J］. IEEE Transactions on Reliability, 2012, 61(1): 50-67.
