AI-driven digital twin for corrosion-fatigue management and opportunistic maintenance in offshore wind turbines

The expansion of offshore wind energy into harsh marine environments is critically challenged by synergistic corrosion-fatigue (C-F), which compromises the structural integrity of turbines. While digital twin (DT) technology offers a promising solution, existing frameworks for C-F prognostic health management are often limited by lack of adaptation to dynamic environmental data and simplistic, deterministic maintenance strategies. To address these deficiencies, this study develops and demonstrates a high-fidelity DT framework founded on three innovations: a coupled-physics model that captures the synergistic feedback between corrosion and fatigue; an artificial intelligence (AI)-driven, Gaussian Process (GP)-based physics-informed machine learning (PIML) engine for real-time environmental adaptation; and a stochastic, opportunistic condition-based maintenance (O-CBM) framework for risk-informed decision-making. These capabilities are demonstrated through a detailed theoretical case study of a floating offshore wind turbine (OWT) tower base, integrating Bayesian inference for model updating with Monte Carlo simulation for lifecycle performance evaluation. Results demonstrate that modeling C-F synergy is critical, reducing predicted service life by 67% compared to fatigue-only analysis, while the O-CBM policy, enabled by the DT's probabilistic intelligence, reduces lifecycle costs by 13.5% and failure risk by 21% over traditional approaches. The study establishes that such an integrated approach, combining coupled physics with AI-driven adaptation and stochastic optimization, is essential for the reliable and economically viable management of offshore assets.