Reliability assessment for multi-state systems with state transition dependency

Abstract

As multi-state system reliability models are capable of characterizing the multi-state nature of engineered systems in deteriorating process, they have received considerable concerns in the past few decades. Most reported works on multi-state system reliability modeling are, however, based on the premise that transitions among states of a system/component are stochastically independent. Sometimes, a system may experience the same environmental/working conditions when deteriorating from a better state to worse ones, and thus, state transitions of a system/component could be stochastically dependent. In this paper, a new reliability model for multi-state systems/components with state transition dependency is put forth. The dependency among state transitions is implicitly characterized by copula functions which offer a great flexibility of linking arbitrary marginal distributions together to construct a multivariate distribution. The reliability function of a population of multi-state systems/components and the conditional reliability function of each individual multi-state system/component given a set of observations are derived. The likelihood functions for model parameter estimation are formulated for two cases, i.e., continuous and discontinuous inspection strategies, and model selection criteria are customized to identify the most preferable model among candidates. Two illustrative examples, together with comparative studies, are presented to demonstrate the effectiveness of the proposed method.