Abstract Offshore mooring systems use chains, wire ropes, and polyester segments to provide seakeeping for floaters. These systems are vulnerable to degradation and failure due to corrosion and fatigue in harsh marine environments. Present day mooring monitoring includes direct and indirect load measurement systems, as well as artificial neural networks (ANNs) for detecting mooring line failures. Direct load measurement systems (e.g., load cells) interfere with mooring line action, are expensive, difficult to maintain, and are often unreliable. Indirect load measurement systems, such as inclinometers, which measure the shape of mooring lines, overcome these shortcomings and are more reliable. ANN-based monitoring systems are useful for detecting line failures after the fact but require considerable training with simulated inputs to be effective. As the offshore renewable energy sector expands, particularly with floating offshore wind turbines (FOWTs), there is an increasing demand for more reliable, cost-effective, and proactive monitoring solutions. This paper presents the latest advances in a physics-based Mooring Condition Monitoring System (MCMS) that offers a scalable and cost-effective solution for real-time mooring integrity assessment. The system utilizes reliable sensors, including Global Positioning System (GPS) and Motion Reference Unit (MRU), that collect data without interfering with operations. Advanced analytics and Digital Twin Technology create a near real-time digital replica of the mooring system, enabling remote monitoring and proactive maintenance. The MCMS integrates a quasi-static mooring model of the system, incorporating corrosion and creep effects. In this study, the MCMS is applied to the operational data from Hywind Scotland Pilot Park, world’s first commercial floating-wind farm to predict mooring line responses.
Das et al. (Sun,) studied this question.