Maintenance policies for systems with condition monitoring and obvious failures

We investigate a maintenance optimization problem with condition monitoring, which allows the decision maker to observe some wear-related variable throughout a system's lifetime to more accurately determine its degree of deterioration. Specifically, we examine the problem of adaptively scheduling observations (both perfect and imperfect) and preventive maintenance actions for a multistate, Markovian deterioration system with obvious failures, such that the long-run average-cost per unit time is minimized. We establish structural properties of the perfect observation-information problem and adjust them for heuristic use in the imperfect observation-information problem. We model both cases as partially observed Markov decision processes and provide numerical examples of optimal and heuristic solutions for both cases.