Mooring line failure diagnosis and motion control of semi-submersible platform based on the predictive model

Abstract

Real-time prediction of deterministic platform motions in the coming future seconds was essential. The long-short-term memory networks (LSTM) and the fully connected neural networks were employed to establish the neural networks for motion predictions. The predicted results were compared with those calculated by AQWA. The results demonstrated that the predictive model could offer a fast and accurate prediction of the motion response of the semi-submersible platform. The sliding window model was built to predict motion without real input and monitor mooring line failure. The results showed that the predictive model based on the sliding window model can also achieve accurate prediction of platform motion. With the help of the predictive model, a control module written in Python was activated on the basis of the identification of mooring line failure. The control results show that the proportional-integral-derivative (PID) controller was very good at recovering the kinematic performance after mooring line failure and suppressing the continuous failure of the remaining mooring lines.