Two-phase degradation data analysis with change-point detection based on Gaussian process degradation model

Abstract Degradation paths of the products exhibiting two-phase patterns are commonly seen in practice due to the changeable internal mechanisms and external environments. In this paper, we propose a two-phase Gaussian process (TPGP) degradation model with a change-point, which comprises the Wiener process-based change-point models as special cases, to describe the degradation paths with two-phase patterns. The change-point is used to represent the transition of degradation characteristics. The degradation rates and variations in the two phases are assumed to be different. Therefore, both monotonically increasing and decreasing or nonmonotonic dispersion trends and complicated auto-correlations in the degradation measurements can be captured by TPGP. Joint methods of the parameter estimation and change-point detection is developed for two different engineering scenarios. The distributions of the first passage time and the remaining useful life are derived in closed-form to promote the mathematical trackability and the applicability of the TPGP model. A comprehensive simulation study shows the effectiveness and validity of the proposed model and method. Finally, we use two real applications to demonstrate the proposed models and methods.

[1]  Lirong Cui,et al.  Two-Phase Degradation Process Model With Abrupt Jump at Change Point Governed by Wiener Process , 2017, IEEE Transactions on Reliability.

[2]  Lirong Cui,et al.  Reliability modeling for a two-phase degradation system with a change point based on a Wiener process , 2020, Reliab. Eng. Syst. Saf..

[3]  Lirong Cui,et al.  Reliability analysis for a Wiener degradation process model under changing failure thresholds , 2018, Reliab. Eng. Syst. Saf..

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

[5]  Jian Liu,et al.  Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model , 2019 .

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

[7]  Yang Liu,et al.  A Novel Lifetime Estimation Method for Two-Phase Degrading Systems , 2019, IEEE Transactions on Reliability.

[8]  Wu Deng,et al.  Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem , 2020, Appl. Soft Comput..

[9]  Yuan Yuan,et al.  Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction , 2017, IEEE Transactions on Reliability.

[10]  Rui Kang,et al.  Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension , 2020, Reliab. Eng. Syst. Saf..

[11]  Donghua Zhou,et al.  A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena , 2017 .

[12]  Venkata Naga Harish Chaluvadi,et al.  Accelerated Life Testing of Electronic Revenue Meters , 2008 .

[13]  Tzu-Liang Tseng,et al.  Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity , 2018, Reliab. Eng. Syst. Saf..

[14]  Suk Joo Bae,et al.  Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests , 2010, IEEE Transactions on Reliability.

[15]  Zhihua Wang,et al.  A generalized degradation model based on Gaussian process , 2018, Microelectron. Reliab..

[16]  Nan Chen,et al.  The Inverse Gaussian Process as a Degradation Model , 2014, Technometrics.

[17]  James Durbin,et al.  The first-passage density of a continuous gaussian process to a general boundary , 1985, Journal of Applied Probability.

[18]  Zheng Lin,et al.  Remaining useful life prediction for multi-phase deteriorating process based on Wiener process , 2021, Reliab. Eng. Syst. Saf..

[19]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[20]  Shing I. Chang,et al.  Optimal Two-Variable Accelerated Degradation Test Plan for Gamma Degradation Processes , 2016, IEEE Transactions on Reliability.

[21]  Suk Joo Bae,et al.  A change-point analysis for modeling incomplete burn-in for light displays , 2006 .

[22]  Suk Joo Bae,et al.  A prediction model of degradation rate for membrane electrode assemblies in direct methanol fuel cells , 2009 .

[23]  Suk Joo Bae,et al.  Bayesian Approach for Two-Phase Degradation Data Based on Change-Point Wiener Process With Measurement Errors , 2018, IEEE Transactions on Reliability.

[24]  Qiang Zhou,et al.  Multiple-Change-Point Modeling and Exact Bayesian Inference of Degradation Signal for Prognostic Improvement , 2019, IEEE Transactions on Automation Science and Engineering.

[25]  Kwok-Leung Tsui,et al.  Condition monitoring and remaining useful life prediction using degradation signals: revisited , 2013 .

[26]  Tangbin Xia,et al.  A hybrid prognostic method based on gated recurrent unit network and an adaptive Wiener process model considering measurement errors , 2021 .

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

[28]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[29]  Dong Wang,et al.  Two novel mixed effects models for prognostics of rolling element bearings , 2018 .

[30]  Tangbin Xia,et al.  Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy , 2017, Reliab. Eng. Syst. Saf..

[31]  Zhengguo Xu,et al.  A model for degradation prediction with change point based on Wiener process , 2015, 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).