A time–frequency physics-informed model for real-time motion prediction of semi-submersibles

Accurate short-term motion prediction is essential for safe offshore operations, and various data-driven models have been developed recently for this purpose. To improve the accuracy, interpretability, and robustness of data-driven models, a novel deep-learning model embedded with the information from time–frequency analysis, named the time–frequency physics-informed (TFPI) model, is proposed to provide physics information and constraints. The measured waves and motions are input to the TFPI model to predict wave-induced motions. Semi-submersible experimental datasets are used for training and testing. Upon verification, the proposed TFPI model demonstrates a strong ability to provide accurate predictions with excellent interpretability. Facilitated by measured waves, the TFPI model successfully extends the forecast period to 2 min with an accuracy exceeding 80%. Moreover, it is robust against different noise levels, whereas the introduction of noise to the training datasets negligibly improves its generalizability. Finally, compared with the long short-term memory model, the proposed TFPI model demonstrates better prediction performance only based on historical information, particularly when the training datasets are limited. The combination of physics-based and deep-learning models is expected to significantly benefit minute-level motion predictions in a wide range of practical applications.