基于深度学习的海工平台运动预测

To improve the safety performance of offshore operation equipment and realize the real-time prediction of motion of offshore structures, a hybrid deep learning model combining convolutional neural network (CNN) and long short-term memory (LSTM) methods is used in this study. The hybrid model extracts the features from motion data by CNN, and utilizes LSTM to learn the temporal relationship among the extracted features. Additionally, Bayesian optimization algorithm is introduced to optimize the hyperparameters of the hybrid model. Firstly, the numerical simulation of the offshore platform is carried out, and the obtained surge motion data is used as experimental data. Secondly, the experimental dataset is divided into training set, verification set and test set. The training and verification set are used for model training and validation to obtain the optimal prediction models for 6 s, 12 s and 18 s of motion. The performance of the developed models is compared with that of the LSTM model using the testing set. The results show that the hybrid model, compared with the LSTM model, can improve the prediction accuracy by 15% to 30% for 6 s, 12 s and 18 s predictions. Furthermore, this study also investigates the relationship between prediction accuracy and input duration as well as prediction duration. The results suggest that the input duration has a minimal impact on the prediction accuracy, while the prediction accuracy shows a linear downward trend with the increase of the prediction duration. Finally, combined with the training time, the hybrid model in this paper demonstrates advantages over LSTM and other models.