Reliability analysis of accelerated life-test data from a repairable system

This paper analyzes accelerating testing of a repairable item modeled by a nonhomogeneous Poisson process with covariates. We extensively analyze a single accelerating variable with two stress levels, and derive closed-form maximum likelihood (ML) solutions. These closed-form solutions provide: (1) an easier way to obtain point estimates of the unknown parameters under usual operating conditions, and (2) a way to obtain confidence intervals on the process parameters and function thereof which are more accurate than those based on asymptotic normality of ML estimates. We analyze the circumstances in which a drawback to closed-form estimation arises, and guides the extent that our procedures may equally apply. An example application drawn from a real situation of accelerated testing is presented, and numerical estimates based on our procedures are derived and discussed. Theoretical and simulation results show that estimation procedures based on the power-law process and regression methods can be a flexible, useful tool for reliability analysis of a repairable item