Maintenance optimization for multi-component systems with a single sensor

Abstract

We consider a multi-component system in which a single sensor monitors a condition parameter. Monitoring gives the decision maker partial information about the system state, but it does not reveal the exact state of the components. Each component follows a discrete degradation process, possibly correlated with the degradation of other components. The decision maker infers a belief about each component’s exact state from the current condition signal and the past data, and uses that to decide when to intervene for maintenance. A maintenance intervention consists of a complete and perfect inspection, and may be followed by component replacements. We model this problem as a partially observable Markov decision process. For a suitable stochastic order, we show that the optimal policy partitions in at most three regions on stochastically ordered line segments. Furthermore, we show that in some instances, the optimal policy can be partitioned into two regions on line segments. In two examples, we visualize the optimal policy. To solve the examples, we modify the incremental pruning algorithm, an exact solution algorithm for partially observable Markov decision processes. Our modification has the potential to also speed up the solution of other problems formulated as partially observable Markov decision processes.