Bayesian heterogeneous degradation performance modeling with an unknown number of sub-populations

Successful modeling of degradation data is of great importance for both accurate reliability assessment and effective maintenance decision-making. Many of existing degradation performance modeling approaches either assume a homogeneous population of units or characterize a heterogeneous population with some restrictive assumptions, such as pre-specifying the number of sub-populations. This paper proposes a Bayesian heterogeneous degradation performance modeling framework to relax the conventional modeling assumptions. Specifically, a Bayesian non-parametric model formulation and learning algorithm are proposed to characterize the historical degradation data of a heterogeneous population of units with an unknown number of homogeneous sub-populations and allowing the joint model estimation and sub-population number identification. Based on the off-line population-level model, an on-line individual-level degradation model with sequential model updating is further developed to improve remaining useful life prediction of individual units with sparse data. A real case study using the heterogeneous degradation data of deteriorating roads is provided to illustrate the proposed approach and demonstrate its validity.