Failure Rate Analysis and AI-Driven Optimization Models for Offshore Wind Turbine Operation and Maintenance

Offshore wind energy is a cornerstone of the global energy transition, yet its economic viability remains constrained by operation and maintenance (O&M) costs, which typically account for 20–30% of total lifecycle expenditure. This article presents an integrated, simulation-based framework that links empirical wind turbine reliability data with techno-economic optimization of offshore O&M strategies. Drawing on maintenance records from three onshore wind farms comprising 97 turbines (145.5 MW) in the province of Burgos, Spain, a subsystem-level reliability model is developed that translates onshore failure statistics into offshore failure rate estimates by means of empirical environmental and power-rating stress factors; the uncertainty inherent in this extrapolation is explicitly quantified through bounding scenarios. A Monte Carlo simulation engine (10,000 iterations) then compares corrective-only, single-visit and dual-visit planned intervention policies over a 25-year project lifetime, showing that a dual-visit strategy improves simulated wind farm availability by 7.2% and reduces the Levelized Cost of Energy (LCOE) by 12.5% relative to corrective-only maintenance. Building on this validated baseline, the article proposes an artificial intelligence (AI)-enhanced predictive maintenance architecture combining SCADA-based gradient-boosted fault classifiers, Bayesian reliability networks and deep reinforcement learning scheduling agents. Evaluated within the same simulation environment, the proposed architecture achieves an additional 13.4% LCOE reduction, i.e. a cumulative reduction of approximately 24% versus corrective-only strategies; these AI results represent simulated expected performance and their field validation with offshore SCADA data is identified as future work. Finally, a sensitivity analysis across eight parameters shows that the weighted average cost of capital (WACC), the capacity factor and turbine reliability dominate LCOE, providing operators and investors with a quantitative basis for prioritizing de-risking, siting and reliability-improvement decisions. The main novelty of the work is the seamless integration—within a single reproducible framework—of empirical failure data, stress-factor reliability translation, stochastic O&M simulation and AI-based scheduling, enabling a like-for-like economic comparison of conventional and intelligent maintenance policies.