Digital Twin-Driven Predictive Maintenance and Fault Diagnosis for Offshore Wind Turbine Systems
Keywords:
digital twin; offshore wind turbine; predictive maintenance; fault diagnosis; data governance; edge-cloud infrastructure; operational resilience; sustainabilityAbstract
Offshore wind turbine systems operate in complex marine environments where accessibility constraints, harsh structural loads, and evolving component degradation make conventional maintenance strategies economically and operationally inefficient. Digital twin technology has emerged as a system-level mechanism for integrating physical assets, operational data, simulation models, and decision support services. This paper presents a structured analysis of digital twin-driven predictive maintenance and fault diagnosis for offshore wind turbines, emphasizing architectural choices, data governance, model deployment, and institutional implications rather than algorithmic derivations. The discussion examines the cyber-physical infrastructure required to synchronize high-fidelity virtual representations with turbine states, the role of supervisory control and data acquisition systems, and the integration of data-driven fault diagnostics with physics-informed degradation models. It further addresses structural trade-offs among edge computing, cloud platforms, latency constraints, and model interpretability in offshore communication environments. Special attention is given to data quality, lifecycle management, interoperability, robustness under distributional shift, fairness in resource allocation across fleets, and the sustainability of digital infrastructure. The paper compares experiences from related industrial sectors and situates offshore wind digital twins within broader policy and standardization debates. It argues that effective deployment depends not only on predictive accuracy but also on trusted data flows, adaptive governance mechanisms, and alignment between maintenance decision support and organizational practices. The conclusion identifies research directions for resilient, equitable, and transparent digital twin ecosystems in offshore wind energy.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
