DYNAMIC CONDITION-BASED MAINTENANCE POLICY FOR DEGRADING SYSTEMS DESCRIBED BY A RANDOM-COEFFICIENT AUTOREGRESSIVE MODEL: A COMPARATIVE STUDY

In this paper, we optimize a dynamic condition-based maintenance policy fora slowly degrading system subject to soft failure and condition monitoring at equidistant, discrete time epochs. A random-coefficient autoregressive model with time effect is developed to describe the system degradation. The system age, previous state observations, and the item-to-item variability of the degradation are jointly combined in the proposed degradation model. Stochastic behavior for both the age-dependent and the state-dependent term are considered, and a Bayesian approach for periodically updating the estimates of the stochastic coefficients is developed to combine information from a degradation database with real-time condition-monitoring information. Based on this degradation model, the dynamic maintenance policy is formulated and solved in a semi-Markov decision process framework. Incorporated with the same semi-Markov decision process framework is a novel approach for mean residual life estimation, which enables simultaneous residual life estimation with the optimization procedure. The effectiveness of using the proposed random-coefficient autoregressive model with time effect rather than the existing fixed-coefficient ones to describe system degradation is demonstrated through a comparative study based on a real degradation dataset. The advantages of using a dynamic maintenance policy are also revealed.