A proactive group maintenance policy for continuously monitored deteriorating systems: Application to offshore wind turbines

This article presents an optimum proactive group maintenance policy for continuously monitored systems affected by stochastic deterioration (degradation). A system is composed of multiple nonidentical subsystems, each exposed to a gradual degradation phenomenon. When the length (or size) of degradation in a subsystem reaches a predetermined fault threshold, it fails and leads to failure of the whole system. In order to avoid system failures and to improve availability levels, a proactive group maintenance task is conducted once the degradation level of a subsystem exceeds an “alert” threshold (smaller than the fault threshold). In this maintenance task, the critical subsystem undergoes a state-dependent repair action, and a preventive maintenance is performed on the other subsystems. Furthermore, the whole system is preventively replaced because of safety requirements when its operational age attains a fixed value. We formulate a multivariate nonlinear maintenance optimization model to simultaneously determine the optimal alert thresholds for subsystems and the replacement time for system. The performance of the proposed maintenance policy, regarding the objective of minimum system’s average long-run maintenance cost per unit time, is compared to five conventional cases of maintenance policies: the reactive response, individual age-based, individual condition-based, bivariate age- and condition-based, and age-based group maintenance. A numerical example, using real-life data collected from an offshore wind dataset, is presented to illustrate the applicability of the proposed model to the maintenance of a group of wind turbine bearings.