(untitled) (International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026), 2026)
Floating offshore wind turbines (FOWTs) rely heavily on mooring systems to maintain position and ensure safe operation under complex environmental loading conditions. However, accurate modeling and condition monitoring of mooring dynamics remain challenging due to strong nonlinear behavior, coupled multi-degree-of-freedom interactions, and the limited availability of high-quality experimental data. This study proposes a data-driven modeling framework for condition monitoring of FOWT mooring systems under limited data conditions. A time-delay neural network (TDNN) is developed to capture the nonlinear dynamic relationships between environmental excitations, platform motions, and surge response which closely correlates with mooring forces. The model is evaluated using the normalized root mean square error (NRMSE) under wave-only, wind-only, and wave–wind coupled scenarios. Results indicate that appropriate selection of input features significantly influences pre diction accuracy. In wave-only cases, pitch motion provides the most rele vant additional information for improving surge prediction. In wind-only cases, the combination of heave and pitch yields the best performance, as these motions contain useful low-frequency dynamics correlated with surge. However, in wave–wind coupled cases, model performance deterio rates due to increased system complexity and limited data length, which reduces generalization capability. Overall, the proposed framework high lights the importance of physically informed input selection and demon strates its effectiveness for data-driven condition monitoring of FOWT mooring systems using limited experimental measurements.