AI-Driven PHM for Floating Offshore Wind Turbines: Review and Main Challenges
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Abstract
Floating Offshore Wind Turbines (FOWTs) offer a transformative solution for capturing wind energy in deep waters, where fixed-bottom installations become economically unfeasible. However, Operations and Maintenance (O&M) costs, which represent up to 30% of total energy costs, remain a major barrier to widespread deployment. The harsh marine environment and limited accessibility demand intelligent and autonomous monitoring systems, making prognostics and health management (PHM) essential for cost-effective FOWTs operations. This paper presents a review of AI-based PHM studies specifically for FOWTs, addressing a significant gap in the existing literature. Particularly, most of existing reviews predominantly focus on offshore operations, digital twin concepts, structural dynamics, or control strategies, none have comprehensively analyzed AI applications tailored to the unique PHM challenges of FOWTs systems. Through a literature review of AI-based PHM studies using the Web of Science and Google Scholar databases, we identify a FOWT-specific monitoring emphasis on structural and station-keeping assets. In addition, we propose a comprehensive end-to-end PHM lifecycle for FOWTs, integrating a hierarchical taxonomy of critical components with a systematic mapping of AI methods to key PHM tasks. By synthesizing the state of the art and identifying critical technological gaps, this work outlines priority research directions essential for enabling reliable, scalable, and autonomous offshore operations.
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Floating Offshore Wind Turbines, Artificial Intelligence, Prognostics and Health Management, Predictive Maintenance
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