Condition based maintenance policies under imperfect maintenance at scheduled and unscheduled opportunities

TL;DR

This paper develops a semi-Markov decision process model for condition-based maintenance in a network of assets, optimizing preventive maintenance scheduling at scheduled and unscheduled opportunities, with applications to wind turbines.

Contribution

It introduces a novel control limit policy framework for maintenance decisions considering imperfect repairs and resource sharing in a network context.

Findings

Optimal policies depend on remaining time until scheduled maintenance.

Sharing maintenance resources reduces long-term costs.

Model applicability demonstrated with wind energy data.

Abstract

Motivated by the cost savings that can be obtained by sharing resources in a network context, we consider a stylized, yet representative model, for the coordination of maintenance and service logistics for a geographic network of assets. Capital assets, such as wind turbines in a wind park, require maintenance throughout their long lifetimes. Two types of preventive maintenance are considered: planned maintenance at periodic, scheduled opportunities, and opportunistic maintenance at unscheduled opportunities. The latter type of maintenance arises due to the network context: when an asset in the network fails, this constitutes an opportunity for preventive maintenance for the other assets in the network. So as to increase the realism of the model at hand and its applicability to various sectors, we consider the option of not-deferring and of deferring planned maintenance after the occurrence of opportunistic maintenance. We also assume that preventive maintenance may not always restore the condition of the system to `as good as new'. By formulating this problem as a semi-Markov decision process, we characterize the optimal policy as a control limit policy (depending on the remaining time until the next planned maintenance) that indicates on the one hand when it is optimal to perform preventive maintenance and on the other hand when maintenance resources should be shared if an opportunity in the network arises. In order to facilitate managerial insights on the effect of each parameter on the cost, we provide a closed-form expression for the long-run rate of cost for any given control limit policy (depending on the remaining time until the next planned maintenance) and compare the costs (under the optimal policy) to these of sub-optimal policies that neglect the opportunity for resource sharing. We illustrate our findings using data from the wind energy industry.