INTRODUCTION: Offshore wind (OsW) energy has emerged as a key factor in the global transition towards high-energy-return renewable energy, with stable winds and minimal land take. However, the Maintenance and Operation (O&M) of OsW wind farms are very challenging due to their hostile marine conditions, huge running costs, and poor accessibility. OBJECTIVES: To address these issues, the proposed method offers a Digital Twin (DT) strategy designed to enhance the remote operation and maintenance (O&M) of OsW wind power equipment. From the use of high-resolution global OSW wind turbine observations from Sentinel-1, combined with domain-specific feature engineering and data preprocessing, including outlier removal and normalization, the approach provides robust input for modelling and analysis. METHODS: One of the most important aspects of the system is the integration of real-time sensor feeds through Arduino devices, secure data exchange through OPC UA, and middleware processing through Node-RED. The sensor-based data architecture feeds into a Unity 3D-based digital twin environment, which continuously synchronizes virtual models with the physical conditions of the turbines. RESULTS: Besides, fault classification is handled with a Categorical Network (CatNet), where attention mechanisms and convolutional layers are used to detect abnormalities such as gearbox faults, generator faults and yaw misalignment. Interactive dashboards, 3D visualization, and predictive analytics are supported within the framework, enabling operators to monitor, diagnose, and control offshore turbines remotely. CONCLUSION: Ultimately, this approach significantly reduces unplanned downtime, enhances safety, and maximizes power output through intelligent, data-driven decision-making.
Si et al. (Mon,) studied this question.