  Real-time motion prediction of a vessel or a floating platform can help to
improve the performance of motion compensation systems. It can also provide
useful early-warning information for offshore operations that are critical with
regard to motion. In this study, a long short-term memory (LSTM) -based machine
learning model was developed to predict heave and surge motions of a
semi-submersible. The training and test data came from a model test carried out
in the deep-water ocean basin, at Shanghai Jiao Tong University, China. The
motion and measured waves were fed into LSTM cells and then went through serval
fully connected (FC) layers to obtain the prediction. With the help of measured
waves, the prediction extended 46.5 s into future with an average accuracy
close to 90%. Using a noise-extended dataset, the trained model effectively
worked with a noise level up to 0.8. As a further step, the model could predict
motions only based on the motion itself. Based on sensitive studies on the
architectures of the model, guidelines for the construction of the machine
learning model are proposed. The proposed LSTM model shows a strong ability to
predict vessel wave-excited motions.

Machine Learning;Machine Learning