[2] PECHT M. Prognostics and health management of electro-nics[M]. New Jersey: Wiley Online Library, 2008.
[3] WU L, FU X, GUAN Y. Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies[J]. Applied Sciences, 2016, 6(6):166-176.
[5] HAO S, YANG J, BERENGUER C. Nonlinear step-stress accelerated degradation modeling considering three sources of variability[J]. Reliability Engineering and System Safety, 2018, 172(1):207-215.
[6] CAI Z Y, CHEN Y X, ZHANG Q, et al. Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error[J]. Journal of Systems Engineering and Electronics, 2017, 28(5): 1028-1038.
[7] SI X, CHEN M, WANG W, et al. Specifying measurement errors for required lifetime estimation performance[J]. European Journal of Operational Research, 2013, 231(3): 631-644.
[8] GEBRAEEL N, LAWLEY M A, LI R, et al. Remining-life distributions from component degradation signals:a Bayesian approach[J]. IIE Transactions, 2005, 37(6): 543-557.
[9] TANG S J, GUO X S, YU C Q, et al. Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors[J]. Journal of Central South University, 2014, 21(9): 4509-4517.
[11] SI X S, HU C H, WANG W, et al. Remaining useful life estimation based on a nonlinear diffusion degradation processes[J]. IEEE Transactions on Reliability, 2012, 61(1): 50-57.
[12] WANG W, CARR M, XU W, et al. A model for remaining life prediction based on Brownian motion with an adaptive drift[J]. Microelectronics Reliability, 2011, 51(2):285-293.