Abstract
In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time ship vertical acceleration prediction algorithm based on the long short-term memory (LSTM) and gated recurrent units (GRU) models of a recurrent neural network. The vertical acceleration time history data at the bow, middle, and stern of a large-scale ship model were obtained by performing a self-propulsion test at sea, and the original data were pre-processed by resampling and normalisation via Python. The prediction results revealed that the proposed algorithm could accurately predict the acceleration time history data of the large-scale ship model, and the root mean square error between the predicted and real values was no greater than 0.1. The optimised multivariate time series prediction program could reduce the calculation time by approximately 55% compared to that of a univariate time series prediction program, and the run time of the GRU model was better than that of the LSTM model.
Highlights
With the rapid economic development occurring worldwide, the scale of maritime transport is constantly expanding, and ship safety requirements are increasing
The data acquisition system and control system were located in the cab; the propulsion system and auto-pilot system were installed in the aft cabin, and three vertical acceleration sensors were arranged in the bow, midship, and stern of the ship model (10% length between perpendiculars (Lpp), 50% Lpp, and 90% Lpp, respectively)
The acceleration time history data (a1, a2, and a3) obtained by the sensors were stored in a computer through the dynamic signal data collector, which provided the original data for the real-time prediction of the ship model vertical acceleration
Summary
With the rapid economic development occurring worldwide, the scale of maritime transport is constantly expanding, and ship safety requirements are increasing. The long short-term memory (LSTM) model is a recurrent neural network (RNN) used in machine learning, which has great potential in time series data prediction [3,4]. The aforementioned research results highlight the favourable prediction effect of the LSTM model on time series data and its high prediction speed. These factors satisfy the accuracy and real-time performance requirements in practice and can be used to predict ship performance. LSTM and GRU neural network models under the framework of TensorFlow were established and were respectively applied to the training and prediction of univariate and multivariate time series of the nonlinear vertical acceleration time history data of large-scale ship models at sea.
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