RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model

Degradation modeling plays an important role in system health diagnosis and remaining useful life (RUL) prediction. Recently, a class of Wiener process models with adaptive drift was proposed for degradation-based RUL prediction, which has been proven flexible and effective. However, the existing studies use an autoregressive model of order 1 for the adaptive drift, which can result in difficulties in both model estimation and RUL prediction. This paper proposes a new adaptive Wiener process model that utilizes a Brownian motion for the adaptive drift. The new model shares the flexibility of the existing models, but avoids the difficulties in model estimation and RUL prediction. A model estimation procedure based on maximum likelihood estimation is developed, and the RUL prediction based on the proposed model is formulated. The effectiveness of the model in RUL prediction is validated using simulation and through an application to the lithium-ion battery degradation data.

[1]  J. L. Folks,et al.  The Inverse Gaussian Distribution and its Statistical Application—A Review , 1978 .

[2]  Suk Joo Bae,et al.  A Nonlinear Random-Coefficients Model for Degradation Testing , 2004, Technometrics.

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

[4]  T.D. Batzel,et al.  Prognostic Health Management of Aircraft Power Generators , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[6]  Yong Sun,et al.  A review on degradation models in reliability analysis , 2010, WCE 2010.

[7]  M. Magnasco,et al.  A Fast Algorithm for the First-Passage Times of Gauss-Markov Processes with Hölder Continuous Boundaries , 2010 .

[8]  Peter W. Tse,et al.  Anomaly Detection Through a Bayesian Support Vector Machine , 2010, IEEE Transactions on Reliability.

[9]  Maurizio Guida,et al.  A State-Dependent Wear Model With an Application to Marine Engine Cylinder Liners , 2010, Technometrics.

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

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

[12]  Donghua Zhou,et al.  A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation , 2013 .

[13]  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..

[14]  Kwok-Leung Tsui,et al.  Degradation Data Analysis Using Wiener Processes With Measurement Errors , 2013, IEEE Transactions on Reliability.

[15]  Mohammad Farrokhi,et al.  Online State-of-Health Estimation of VRLA Batteries Using State of Charge , 2013, IEEE Transactions on Industrial Electronics.

[16]  Liang Tang,et al.  Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries , 2013, IEEE Transactions on Industrial Electronics.

[17]  N. Balakrishnan,et al.  Bivariate degradation analysis of products based on Wiener processes and copulas , 2013 .

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

[19]  Loon Ching Tang,et al.  Semiparametric Estimation of Gamma Processes for Deteriorating Products , 2014, Technometrics.

[20]  Jian Ma,et al.  A new neural network model for the state-of-charge estimation in the battery degradation process , 2014 .

[21]  Xiaosong Hu,et al.  Model-Based Dynamic Power Assessment of Lithium-Ion Batteries Considering Different Operating Conditions , 2014, IEEE Transactions on Industrial Informatics.

[22]  Kwok L. Tsui,et al.  A naive Bayes model for robust remaining useful life prediction of lithium-ion battery , 2014 .

[23]  Donghua Zhou,et al.  Estimating Remaining Useful Life With Three-Source Variability in Degradation Modeling , 2014, IEEE Transactions on Reliability.

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

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

[26]  Youxian Sun,et al.  Remaining Useful Life Prediction for a Nonlinear Heterogeneous Wiener Process Model With an Adaptive Drift , 2015, IEEE Transactions on Reliability.

[27]  Min Xie,et al.  Stochastic modelling and analysis of degradation for highly reliable products , 2015 .

[28]  Joseph Mathew,et al.  A review on prognostic techniques for non-stationary and non-linear rotating systems , 2015 .

[29]  Q. Miao,et al.  Some Improvements on a General Particle Filter Based Bayesian Approach for Extracting Bearing Fault Features , 2015 .

[30]  Yaguo Lei,et al.  An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.

[31]  Tao Yuan,et al.  A Bayesian approach to modeling two-phase degradation using change-point regression , 2015, Reliab. Eng. Syst. Saf..

[32]  M. Abundo.  On the first-passage time of an integrated Gauss-Markov process , 2015, 1506.01155.

[33]  Nan Chen,et al.  A new class of Wiener process models for degradation analysis , 2015, Reliab. Eng. Syst. Saf..

[34]  Xiao-Sheng Si,et al.  An Adaptive Prognostic Approach via Nonlinear Degradation Modeling: Application to Battery Data , 2015, IEEE Transactions on Industrial Electronics.

[35]  Laifa Tao,et al.  Similarity recognition of online data curves based on dynamic spatial time warping for the estimation of lithium-ion battery capacity , 2015 .

[36]  Yanyang Zi,et al.  A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem , 2016, IEEE Transactions on Industrial Informatics.

[37]  Jun Yang,et al.  Measurement errors in degradation-based burn-in , 2016, Reliab. Eng. Syst. Saf..

[38]  Yaguo Lei,et al.  A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction , 2016, IEEE Transactions on Instrumentation and Measurement.

[39]  Rui Peng,et al.  Age-Based Replacement Policy With Consideration of Production Wait Time , 2016, IEEE Transactions on Reliability.

[40]  I. Villarreal,et al.  Critical review of state of health estimation methods of Li-ion batteries for real applications , 2016 .

[41]  Yaguo Lei,et al.  A Model-Based Method for Remaining Useful Life Prediction of Machinery , 2016, IEEE Transactions on Reliability.

[42]  Dong Wang,et al.  Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter , 2016, IEEE Transactions on Instrumentation and Measurement.

[43]  Weiwen Peng,et al.  Bivariate Analysis of Incomplete Degradation Observations Based on Inverse Gaussian Processes and Copulas , 2016, IEEE Transactions on Reliability.

[44]  Weiwen Peng,et al.  Bayesian Degradation Analysis With Inverse Gaussian Process Models Under Time-Varying Degradation Rates , 2017, IEEE Transactions on Reliability.