Multi-objective Optimization Study of Water-lubricated Bearing Structure for Ships Based on Machine Learning

Water-lubricated bearing is an important supporting component for the safe and stable operation of ship shaft systems, and it is an effective measure to ensure the safe navigation of ships and improve bearing performance. The PSO-BP neural network is introduced to establish an agent model of load-carrying capacity and friction force of water-lubricated bearings. The NSGA-II algorithm is applied to take the maximum load-carrying capacity output and the minimum friction force output of the agent model. Then the input of the agent model is optimized, and the Pareto solution of the bearings is obtained. The optimal compromise solution in the Pareto solution set is selected using the TOPSIS method. The results show that the optimized bearing load carrying capacity is increased by 38.17%, and friction force is reduced by 2.23% compared with the original design of the bearing. The percentage difference in optimization results is less than 7% between the agent and the simulation model. © 2024 Editorial office of Ship Building of China. All rights reserved.