Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model
When ships conduct offshore operations in the ocean, they are subject to disturbances from natural factors such as sea breezes and waves. These disturbances lead to movements detrimental to the ship’s stability, especially heave movement in the vertical direction, which profoundly impacts the safety...
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1008 |
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| author | Guowei Li Gang Tang Jingyu Zhang Qun Sun Xiangjun Liu |
| author_facet | Guowei Li Gang Tang Jingyu Zhang Qun Sun Xiangjun Liu |
| author_sort | Guowei Li |
| collection | DOAJ |
| description | When ships conduct offshore operations in the ocean, they are subject to disturbances from natural factors such as sea breezes and waves. These disturbances lead to movements detrimental to the ship’s stability, especially heave movement in the vertical direction, which profoundly impacts the safety of shipboard facilities and staff. To counter this, the active wave compensation device is widely used on ships to maintain the stability of the working environment. However, the system’s efficiency and accuracy are compromised by the significant delay incurred while obtaining real-time motion signals and driving the actuator for motion compensation. To solve the time delay problem of shipborne wave compensation equipment in motion compensation under complex sea conditions, it is necessary to improve the ship heave motion prediction accuracy in an active wave compensation system. This paper presents a prediction method of ship heave motion based on the particle swarm optimization (PSO) and convolutional neural network–long short-term memory (CNN-LSTM) hybrid prediction model. The paper begins by establishing the ship heave motion model based on the P–M spectrum and slice theory, simulating the ship heave motion curve under different sea conditions on MATLAB. This simulation provides crucial data for the subsequent prediction model. The paper then delves into the realization method of ship heave motion based on PSO-CNN-LSTM, where the convolutional neural network (CNN) is used to extract the features of the input signal, thereby enhancing the multi-source feature fusion ability of the LSTM neural network model. The PSO algorithm is then employed to optimize the network structure and hyperparameters of the convolutional neural network. The experiments demonstrate that the proposed PSO-CNN-LSTM hybrid model effectively addresses the problem of predicting drift and boasts significantly higher prediction accuracy, making it suitable for predicting the short-term heave motion of ships. The data show that the optimized root mean square error (RMSE) value under level 5 sea conditions is 0.01265 compared to 0.01673 before optimization, and the optimized RMSE value under level 6 sea conditions is 0.01140 compared to 0.01479 before optimization, which demonstrates that the error between the predicted value and the actual value of the model decreases. This improved accuracy provides reassurance in the model’s predictive capabilities and lays the foundation for improving the accuracy of the motion compensation system in the future. |
| format | Article |
| id | doaj-art-748e43ce0362437c8a78fceb1eddc246 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-748e43ce0362437c8a78fceb1eddc2462025-08-20T02:21:13ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-01136100810.3390/jmse13061008Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM ModelGuowei Li0Gang Tang1Jingyu Zhang2Qun Sun3Xiangjun Liu4Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaMechnical College, Shanghai Dianji University, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaWhen ships conduct offshore operations in the ocean, they are subject to disturbances from natural factors such as sea breezes and waves. These disturbances lead to movements detrimental to the ship’s stability, especially heave movement in the vertical direction, which profoundly impacts the safety of shipboard facilities and staff. To counter this, the active wave compensation device is widely used on ships to maintain the stability of the working environment. However, the system’s efficiency and accuracy are compromised by the significant delay incurred while obtaining real-time motion signals and driving the actuator for motion compensation. To solve the time delay problem of shipborne wave compensation equipment in motion compensation under complex sea conditions, it is necessary to improve the ship heave motion prediction accuracy in an active wave compensation system. This paper presents a prediction method of ship heave motion based on the particle swarm optimization (PSO) and convolutional neural network–long short-term memory (CNN-LSTM) hybrid prediction model. The paper begins by establishing the ship heave motion model based on the P–M spectrum and slice theory, simulating the ship heave motion curve under different sea conditions on MATLAB. This simulation provides crucial data for the subsequent prediction model. The paper then delves into the realization method of ship heave motion based on PSO-CNN-LSTM, where the convolutional neural network (CNN) is used to extract the features of the input signal, thereby enhancing the multi-source feature fusion ability of the LSTM neural network model. The PSO algorithm is then employed to optimize the network structure and hyperparameters of the convolutional neural network. The experiments demonstrate that the proposed PSO-CNN-LSTM hybrid model effectively addresses the problem of predicting drift and boasts significantly higher prediction accuracy, making it suitable for predicting the short-term heave motion of ships. The data show that the optimized root mean square error (RMSE) value under level 5 sea conditions is 0.01265 compared to 0.01673 before optimization, and the optimized RMSE value under level 6 sea conditions is 0.01140 compared to 0.01479 before optimization, which demonstrates that the error between the predicted value and the actual value of the model decreases. This improved accuracy provides reassurance in the model’s predictive capabilities and lays the foundation for improving the accuracy of the motion compensation system in the future.https://www.mdpi.com/2077-1312/13/6/1008heave motion predictionlong short-term memory network (LSTM)convolutional neural network (CNN)particle swarm optimization (PSO)hybrid prediction model |
| spellingShingle | Guowei Li Gang Tang Jingyu Zhang Qun Sun Xiangjun Liu Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model Journal of Marine Science and Engineering heave motion prediction long short-term memory network (LSTM) convolutional neural network (CNN) particle swarm optimization (PSO) hybrid prediction model |
| title | Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model |
| title_full | Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model |
| title_fullStr | Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model |
| title_full_unstemmed | Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model |
| title_short | Short-Term Prediction of Ship Heave Motion Using a PSO-Optimized CNN-LSTM Model |
| title_sort | short term prediction of ship heave motion using a pso optimized cnn lstm model |
| topic | heave motion prediction long short-term memory network (LSTM) convolutional neural network (CNN) particle swarm optimization (PSO) hybrid prediction model |
| url | https://www.mdpi.com/2077-1312/13/6/1008 |
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