Benchmark Dataset for Offshore Platform Motion Prediction and Its Applications

Abstract

The accurate prediction of offshore platform and ship motion is crucial for motion compensation devices and for helping the crew make informed decisions. Traditional time series and physical models are being replaced by machine learning models due to their simplicity and lower training cost. However, insufficient data has hindered model training, making evaluating and comparing different models difficult. This paper introduces a comprehensive motion dataset containing data of more than 400 pieces from tens of offshore platform tests conducted at the State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University. The dataset is divided into subsets tailored for four application scenarios, including specific types of offshore platforms, wave conditions, noise addition data, and transfer learning. A Convolutional Attention-based LSTM model that combines convolution and self-attention mechanisms is proposed to validate the dataset and improve the accuracy of motion prediction. The proposed model is compared with classical models using our introduced dataset, achieving 5–10% improvement and confirming the dataset’s high reliability and applicability, as well as the effectiveness of the Conv-Att-LSTM model. This development sets a new standard for motion prediction and furthers the application of machine learning in ocean engineering.