Residual lifetime prediction for heterogeneous degradation data by Bayesian semi-parametric method

Abstract: Degradation data are considered for assessing reliability in highly reliable systems. The usual assumption is that degradation units come from a homogeneous population. But in presence of high variability in the manufacturing process, this assumption is not true in general; that is different sub-populations are involved in the study. Predicting residual lifetime of a functioning unit is a major challenge in the degradation modeling especially in heterogeneous environment. To account for heterogeneous degradation data, we have proposed a Bayesian semi-parametric approach to relax the conventional modeling assumptions. We model the degradation path using Dirichlet process mixture of normal distributions. Based on the samples obtained from posterior distribution of model parameters we obtain residual lifetime distribution for individual unit. Transformation based MCMC technique is used for simulating values from the derived residual lifetime distribution for prediction of residual lifetime. A simulation study is undertaken to check performance of the proposed semi-parametric model compared with parametric model. Fatigue Crack Size data is analyzed to illustrate the proposed methodology.