A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States

TL;DR

This paper introduces a hybrid neural network approach combining LSTM and CNN to accurately predict multi-step ship roll motions in high sea states, enhancing safety and operability.

Contribution

A novel hybrid LSTM-CNN model for multi-step prediction of ship roll motion in high sea states, leveraging combined nonlinear and hydrodynamic memory features.

Findings

The proposed method outperforms single LSTM and CNN models.

Accurate prediction of at least one period of roll motion.

Feature selection of motion states and wave heights improves accuracy.

Abstract

Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions in high sea states. A hybrid neural network is proposed that combines long short-term memory (LSTM) and convolutional neural network (CNN) in parallel. The motivation is to extract the nonlinear dynamic characteristics and the hydrodynamic memory information through the advantage of CNN and LSTM, respectively. For the feature selection, the time histories of motion states and wave heights are selected to involve sufficient information. Taken a scaled KCS as the study object, the ship motions in sea state 7 irregular long-crested waves are simulated and used for the validation. The results show that at least one period of roll motion can be accurately predicted. Compared with the single LSTM and CNN methods, the proposed method has better performance in predicting the amplitude of roll angles. Besides, the comparison results also demonstrate that selecting motion states and wave heights as feature space improves the prediction accuracy, verifying the effectiveness of the proposed method.