AI-driven predictive maintenance in ocean energy systems

Ocean Energy Systems (OES), including tidal, wave, and offshore wind technologies, are gaining attention as reliable sources of renewable energy. However, when utilized in extreme maritime settings, the machinery experiences accelerated wear and tear, highlighting the importance of diligent servicing. This paper presents a predictive maintenance framework leveraging artificial intelligence (AI) and machine learning (ML) algorithms to ensure the operational reliability of ocean energy assets. Real-time information, such as data on vibration, corrosion levels, temperature fluctuations, and flow rates, is gathered from turbines, generators, and mooring systems to be used in this proposed technique. Diverse supervised learning algorithms, such as Random Forest, LSTM, and Gradient Boosting, are instructed with previous failure datasets to anticipate impending malfunctions proactively. Unsupervised anomaly detection strategies are also employed to pinpoint unforeseen types of failures.  The system's effectiveness is confirmed through field simulations and virtual sensor modelling, which demonstrate a 30–45% increase in maintenance scheduling efficiency and a notable decrease in unscheduled shutdowns. Consequently, this AI-based strategy presents a cost-effective and scalable solution for supporting sustainable practices in the ocean energy sector.