Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion

Most of the maintenance optimization models in condition-based maintenance (CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model (HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo (MCMC) simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model (HMM), the age-based replacement policy, Hotelling's T², multivariate exponentially weihted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.