Toward Intelligent Prognostics and Health Management for Floating Offshore Wind Turbines
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Abstract
Floating offshore wind turbines (FOWTs) enable the exploitation of deep-water wind resources where conventional fixed-bottom foundations become technically or economically infeasible. While this technology significantly expands the potential of offshore renewable energy, it also introduces new challenges for reliable operation and maintenance due to harsh marine environments, complex aero–hydro–servo–elastic dynamics, and limited operational data availability. Although AI-driven Prognostics and Health Management (PHM) has achieved substantial progress for conventional wind turbines, its application to FOWTs remains relatively limited. This doctoral research proposes an intelligent PHM framework specifically developed for floating wind systems, addressing key challenges related to limited operational data and domain knowledge, heterogeneous and unreliable monitoring data, and dynamic environmental complexity. Domain adaptation combined with knowledge graph construction, multimodal learning for heterogeneous data integration, and physics-informed machine learning for structural dynamic modeling are investigated as complementary methodological contributions. These components are progressively integrated into a unified PHM lifecycle pipeline supporting fault detection, remaining useful life prediction, and maintenance decision support for FOWTs.
How to Cite
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Floating Offshore Wind Turbines, Artificial In-telligence, Prognostics and Health Management, PredictiveMaintenance
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