Residual life estimation under time-varying conditions based on a Wiener process

ABSTRACT Residual life (RL) estimation plays an important role in prognostics and health management. In operating conditions, components usually experience stresses continuously varying over time, which have an impact on the degradation processes. This paper investigates a Wiener process model to track and predict the RL under time-varying conditions. The item-to-item variation is captured by the drift parameter and the degradation characteristic of the whole population is described by the diffusion parameter. The bootstrap method and Bayesian theorem are employed to estimate and update the distribution parameters of ‘a’ and ‘b’, which are the coefficients of the linear drifting process in the degradation model. Once new degradation information becomes available, the RL distributions considering the future operating condition are derived. The proposed method is tested on Lithium-ion battery devices under three levels of charging/discharging rates. The results are further validated by a simulation method.

[1]  M-Y You,et al.  Approaches for component degradation modelling in time-varying environments with application to residual life prediction , 2012 .

[2]  Nagi Gebraeel,et al.  Sensory-Updated Residual Life Distributions for Components With Exponential Degradation Patterns , 2006, IEEE Transactions on Automation Science and Engineering.

[3]  G A Whitmore,et al.  Modelling Accelerated Degradation Data Using Wiener Diffusion With A Time Scale Transformation , 1997, Lifetime data analysis.

[4]  Bo Guo,et al.  Residual life estimation based on nonlinear-multivariate Wiener processes , 2015 .

[5]  Bo Guo,et al.  Residual life estimation based on a generalized Wiener degradation process , 2014, Reliab. Eng. Syst. Saf..

[6]  Sheng-Tsaing Tseng,et al.  Mis-Specification Analysis of Linear Degradation Models , 2009, IEEE Transactions on Reliability.

[7]  Rong Li,et al.  Residual-life distributions from component degradation signals: A Bayesian approach , 2005 .

[8]  B. Guo,et al.  Residual life estimation based on bivariate Wiener degradation process with time-scale transformations , 2014 .

[9]  Mohamed Ahwiadi,et al.  An Enhanced Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction , 2019, IEEE Transactions on Instrumentation and Measurement.

[10]  Jing Pan,et al.  Prognostic Degradation Models for Computing and Updating Residual Life Distributions in a Time-Varying Environment , 2008, IEEE Transactions on Reliability.

[11]  Wei Liang,et al.  Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..

[12]  Wenbin Wang,et al.  A model for residual life prediction based on Brownian motion with an adaptive drift , 2011, Microelectron. Reliab..

[13]  Yu Peng,et al.  Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..

[14]  N. Balakrishnan,et al.  Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process , 2012 .

[15]  Dirk Uwe Sauer,et al.  Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data , 2012 .

[16]  Hongzhou Wang,et al.  A survey of maintenance policies of deteriorating systems , 2002, Eur. J. Oper. Res..

[17]  Kwok-Leung Tsui,et al.  An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..

[18]  Zhongbao Zhou,et al.  A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries in spacecraft , 2013, Reliab. Eng. Syst. Saf..

[19]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[20]  Wilson Wang,et al.  A Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction , 2014, IEEE Transactions on Instrumentation and Measurement.

[21]  Xiao Wang,et al.  Wiener processes with random effects for degradation data , 2010, J. Multivar. Anal..

[22]  Donghua Zhou,et al.  A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution , 2013, Eur. J. Oper. Res..

[23]  Steven M. Cox,et al.  Stochastic models for degradation-based reliability , 2005 .

[24]  Noureddine Zerhouni,et al.  Prognostics of PEM fuel cell in a particle filtering framework , 2014 .

[25]  Xue Wang,et al.  Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error , 2014 .

[26]  M. Verbrugge,et al.  Degradation of lithium ion batteries employing graphite negatives and nickel-cobalt-manganese oxide + spinel manganese oxide positives: Part 1, aging mechanisms and life estimation , 2014 .