Predicting heave and surge motions of a semi-submersible with neural networks

TL;DR

This paper presents an LSTM-based machine learning model capable of accurately predicting semi-submersible vessel heave and surge motions up to 46.5 seconds ahead, aiding offshore operations and motion compensation.

Contribution

The study develops and validates a novel LSTM neural network model for real-time prediction of vessel motions using measured wave data, including noise robustness and architecture guidelines.

Findings

Achieves ~90% prediction accuracy 46.5 seconds ahead.

Effective noise handling up to a noise level of 0.8.

Model predicts motions solely based on past motion data.

Abstract

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.