BAYBUTT M, MINNELLA C, GINART A E, et al. Improving digital system diagnostics through prognostic and health management (PHM) technology. IEEE Trans. on Instrumentation and Measurement, 2009, 58 (2): 255- 262.
|2||BATZEL T D, SWANSON D C. Prognostic health management of aircraft power generators. IEEE Trans. on Aerospace and Electronic Systems, 2009, 45 (04): 473- 482.|
|3||CHOOKAH M, NUHI M, MODARRESN M. A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue. Reliability Engineering and System Safety, 2011, 96 (7): 1601- 1610.|
|4||YAO L, SUN J. The study of prognostic and health management system for smart grid based on wireless sensor networks. Applied Mechanics and Materials, 2015, 719 (1): 426- 430.|
|5||HAROON K, AlGAYEM Q, RICHARDSON A M. A housekeeping prognostic health management framework for microfluidic systems. EEE Trans. on Device and Materials Reliability, 2017, 17 (2): 438- 449.|
ANDRES M, GIOVANNI J. Prognostic and health management system for fly-by-wire electro-hydraulic servo actuators for detection and tracking of actuator faults. Procedia CIRP, 2017, 59, 116- 121.
|7||TSENG K, LIANG J, CHANG W. Regression models using fully discharged voltage and internal resistance for state of health estimation of Lithium-Ion batteries. Energies, 2015, 8 (4): 2889- 2907.|
LEI Y, LI N, GONTARZ S, et al. A model-based method for remaining useful life prediction of machinery. IEEE Trans. on Reliability, 2016, 65 (3): 1314- 1326.
WU L, FU X, GUAN Y. Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies. Applied Sciences, 2016, 6 (6): 166- 176.
|10||LIU D, ZHOU J, PAN D, et al. Lithium-ion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning. Measurement, 2015, 63 (3): 143- 151.|
|11||PHAN T T, HEALEY J T, KENT W R. Microelectronic manufacturing yield, reliability, and failure analysis-prevention of auto-doping-induced threshold voltage shifts. SPIE International Society for Optical Engineering, 1995, 2635, 136- 144.|
|12||URSUTIU D, JONES B K. Low frequency noise used as a lifetime test of LEDs. Journal of Applied Physics, 2004, 96 (2): 966- 969.|
|13||PARK C, PADGETT W J. Accelerated degradation models for failure based on geometric Brownian motion and Gamma processes. Lifetime Data Analysis, 2005, 11 (4): 511- 527.|
|14||TSENG S T, TANG J, KU I H. Determination of optimal burn-in parameters and residual life for highly reliable products. Naval Research Logistics, 2003, 50 (1): 1- 14.|
|15||WANG X, NAIR V. A class of degradation model based on nonhomogeneous Gaussian process. Ann Arbor, America: University of Michigan, 2005.|
|16||CAI Z Y, XIANG H C, WANG P, et al. Missile storage lifetime assessment of multivariate degradation modeling under competition failure. Systems Engineering and Electronics, 2018, 40 (5): 1183- 1189.|
|17||LI Z D, ZHANG T. Optimization of inspection and repair of multi-state system under imperfect characteristics. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43 (5): 951- 961.|
|18||PENG C, TSENG S. Mis-specification analysis of linear degradation models. IEEE Trans. on Reliability, 2009, 58 (3): 444- 455.|
|19||TANG S J, GUO X, YU C, et al. Accelerated degradation tests modeling based on the nonlinear Wiener process with random effects. Mathematical Problems in Engineering, 2014, 2014 (2): 1- 11.|
|20||YE Z, CHEN N, TSUI K L. A Bayesian approach to condition monitoring with imperfect inspections. Quality and Reliability Engineering International, 2015, 31 (3): 513- 522.|
|21||MERRITT B T, WHITHAM K. Performance and cost analysis of large capacitor banks using Weibull statistics and MTBF. Proc. of the IEEE International Pulsed Power Conference, 1981.|
|22||LARSON D W, MACDOUGALL F W, HARDY P. The impact of high energy density capacitors with metallized electrode in large capacitor banks for nuclear fusion application. Proc. of the 9th IEEE International Pulsed Power Conference, 1993, 735- 742.|
|23||SARIEANT W J, ZIMHELD J, MACDOUGALL F W. Capacitors. IEEE Trans. on Plasma Science, 1998, 26 (5): 1368- 1392.|
|24||ZHAO J Y, LIU F. Reliability assessment of the metallized film capacitors from degradation data. Microelectronic Reliability, 2007, 47 (2/3): 434- 436.|
CHHIKARA R S, FOLKS J L. The inverse gaussian distribution as a lifetime model. Technometrics, 1977, 19 (4): 461- 468.
DOKSUM K A, HOYLAND A. Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse Gaussian distribution. Technometrics, 1992, 34 (1): 74- 82.
LIAO H, ELSAYED E A. Reliability inference for field conditions from accelerated degradation testing. Naval Research Logistics, 2006, 53 (6): 576- 587.
|28||WANG X, JIANG P, GUO B, et al. Real-time reliability evaluation for an individual product based on change-point Gamma and Wiener process. Quality and Reliability Engineering International, 2014, 30 (4): 513- 525.|
|29||WANG H W, XU T X, WANG W Y. Remaining life prediction based on Wiener processes with ADT prior information. Quality and Reliability Engineering International, 2016, 32 (4): 753- 765.|
KAISER K A, GEBRAEEL N Z. Predictive maintenance management using sensor-based degradation models. IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans, 2009, 39 (4): 840- 849.
|31||WANG X. Wiener processes with random effects for degradation data. Journal of Multivariate Analysis, 2010, 101 (2): 340- 351.|
SI X S, WANG W B, HU C H, et al. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Trans. on Reliability, 2012, 61 (1): 50- 67.
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. Journal of Central South University, 2014, 21 (12): 4509- 4517.
|34||JIANG R. Optimization of alarm threshold and sequential inspection scheme. Reliability Engineering and System Safety, 2010, 95 (3): 208- 215.|
|35||SI X S, WANG W B, HU C H, et al. Remaining useful life estimation, a review on the statistical data driven approaches. European Journal of Operational Research, 2011, 213 (1): 1- 14.|
|36||YE Z S, WANG Y, TSUI K L, et al. Degradation data analysis using Wiener processes with measurement errors. IEEE Trans. on Reliability, 2013, 62 (4): 772- 780.|
|37||WANG Y, YE Z S, TSUI K L. Stochastic evaluation of magnetic head wears in hard disk drives. IEEE Trans. on Magnetics, 2014, 50 (5): 1- 7.|
|38||WANG X L, JIANG P, GUO B, et al. Real-time reliability evaluation based on damaged measurement degradation data. Journal of Central South University, 2012, 19 (11): 3162- 3169.|
|39||WANG W, CARR M, XU W, et al. A model for residual life prediction based on Brownian motion with an adaptive drift. Microelectronics Reliability, 2011, 51 (2): 28- 293.|
WEI M H, CHEN M, ZHOU D. Multi-sensor information based remaining useful life prediction with anticipated performance. IEEE Trans. on Reliability, 2013, 62 (1): 183- 198.
|41||PENG W, DAVID W. Reliability and degradation modeling with random or uncertain failure threshold. Proc. of the Reliability and Maintainability Symposium, 2007, 392- 397.|
|42||USYNIN A, HIENES J W, URMANOV A. Uncertain failure thresholds in cumulative damage models. Proc. of the Reliability and Maintainability Symposium, 2008, 334- 340.|
HUANG J B, KONG D J, CUI L R. Bayesian reliability assessment and degradation modeling with calibrations and RFT. Journal of Shanghai Jiaotong University, 2016, 21 (4): 478- 483.
WEI M, CHEN M, ZHOU D. Multi-sensor information based remaining useful life prediction with anticipated performance. IEEE Trans. on Reliability, 2013, 62 (1): 183- 198.
TANG S J, YU C Q, FENG Y B, et al. Remaining useful life estimation based on Wiener degradation processes with RFT. Journal of Central South University, 2016, 23 (9): 2230- 2241.
|46||EMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 1977, 39 (1): 1- 38.|
|47||TANG S J, YU C Q, WANG X, et al. Remaining useful life prediction of lithium-ion batteries based on the Wiener process with measurement error. Energies, 2014, 7 (2): 520- 547.|
|48||GREENE W H. Econometric analysis. New Jersey: Prentice-Hall, 2003.|
|49||SAXENA A, KAI G, SIMON D, et al. Damage propagation modeling for aircraft engine run-to-failure simulation. Proc. of the International Conference on Prognostics and Health Management, 2008: 1-9.|
|50||DAVID R I, RUGGERI F, MICHAEL P W. Bayesian analysis of stochastic process model. New York: Wiley, 2012.|
|51||NELSON W. Applied life data analysis. New York: John Wiley & Sons, 1982.|
|||Zhongyi CAI, Zezhou WANG, Yunxiang CHEN, Jiansheng GUO, Huachun XIANG. Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling [J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 194-205.|
|||Zhongyi Cai, Yunxiang Chen, Qiang Zhang, and Huachun Xiang. Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error [J]. Systems Engineering and Electronics, 2017, 28(5): 1028-1038.|