Opportunistic Maintenance Optimization of Offshore Wind Turbine with Deep Reinforcement Learning

Offshore wind turbine operation and maintenance (O&M) is characterized by high costs and restricted maintenance windows, making multi-component opportunistic maintenance an important strategy for reducing O&M expenditure. Existing opportunistic maintenance strategies, however, often rely on static thresholds and stepwise optimization procedures, and they have limited capability to accommodate component heterogeneity. To address these limitations, this study proposes a deep reinforcement learning (DRL) framework for identifying the cost-optimal preventive opportunistic maintenance (POM) strategy. The POM problem is formulated as a Markov decision process, in which preventive replacement, imperfect maintenance, and no-maintenance actions are considered. A deep Q network is employed to train the agent to dynamically determine maintenance actions and timing within opportunity windows, thereby optimizing maintenance decisions. A case study involving eight non-critical components of a wind turbine is conducted, and the proposed method is compared with a static-threshold heuristic strategy and a conventional preventive replacement maintenance strategy. The results demonstrate the advantages of DRL in developing the optimal opportunistic maintenance strategy for offshore wind turbines and provide decision support for intelligent O&M of multi-component systems.