Prediction of underwater towing cable tension based on LSTM neural network

Xin Zhang; Leilei Dong; Qi Zhang; Wenxuan He; Juwei Sun; Xiangkai Li

doi:10.1117/12.2684926

19 October 2023 Prediction of underwater towing cable tension based on LSTM neural network

Xin Zhang, Leilei Dong, Qi Zhang, Wenxuan He, Juwei Sun, Xiangkai Li

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270909 (2023) https://doi.org/10.1117/12.2684926
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Aiming at the problems that the tension of underwater towing cable is not easy to monitor and the calculation time of the time domain coupling analysis process of towing cable is long, a tension early warning system of underwater towing cable based on long short-term memory (LSTM) neural network is designed. This paper considers the influence of single time variable and multiple variables (six degrees of freedom of tugboat) respectively. The tension time series of the key nodes of the towing cable are predicted and analyzed, and the predicted results are compared with the tension data obtained based on OrcaFlex numerical simulation to verify the accuracy of the prediction. Meanwhile, the predicted results are compared with the back propagation (BP) model and the kernel extreme learning machine (KEML) model. The experimental results show that the prediction accuracy based on LSTM neural network has obvious advantages, the maximum determination coefficient R² is 0.8787, and the minimum root mean square error is 4.9082, which can achieve the effect of tension prediction, and has certain significance for the early warning of underwater towing cable tension.

Citation Download Citation

Xin Zhang, Leilei Dong, Qi Zhang, Wenxuan He, Juwei Sun, and Xiangkai Li "Prediction of underwater towing cable tension based on LSTM neural network", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270909 (19 October 2023); https://doi.org/10.1117/12.2684926

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