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.