Fault prediction of marine LNG dual fuel engine based on LSTM-PSO-SVM
In order to solve the problems of complex equipment,low efficiency and poor accuracy of fault prediction for marine LNG dual-fuel engine,a prediction model combining long-short term memory network(LSTM)and improved particle swarm optimization algorithm(PSO)to optimize the model of support vector machine(SVM)was proposed.The time series capability of the LSTM model is used to predict the future operating state of the device,and then the SVM para-meters are optimized using a non-linear adaptive inertia weight improvement PSO algorithm to improve the optimization power and convergence rate.The improved LSTM-PSO-SVM fusion model can predict the equipment fault state quickly and accurately.Through the fault prediction simulation of a low-speed Marine LNG dual-fuel engine,the results show that the above model has accurate and efficient prediction ability,and can accurately identify potential faults of Marine LNG dual-fuel engine.
万方数据
维普
