Short-term prediction of trimaran load based on data driven technology

Due to the complex flow interference by side hulls, load prediction has been one of the obstacles to achieve structural health monitoring and intelligent navigation of trimarans. According to the machine learning theory, a short-term prediction method towards trimaran loads is studied by using an optimized data-driven model. In the research, the monitoring data from a trimaran model test is applied for the training and testing of long short-term memory (LSTM) neural network. The impact analysis on the factors such as input length, neuron number, artificial neural network (ANN) optimizer, and output scope, are taken. To highlight the trimaran high-frequency load fluctuation and improve the prediction accuracy, the LSTM neural network combines with different signal decomposition algorithms, such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). Through the comprehensive comparison of statistical indicators at different positions and wave environment, the VMD-LSTM model is selected by considering its highest load prediction ability among these artificial neural network models. The research will improve the load prediction accuracy in the structural health monitoring systems and offer effective technical support for intelligent unmanned trimarans.