Research on Intelligent Fault Diagnosis and Predictive Maintenance Model in Ship Power System

This paper proposes an intelligent fault diagnosis and predictive maintenance model based on decision tree combined with random forest algorithm. Through multi-dimensional analysis of ship power system operation data, the model can effectively identify potential faults in the system and achieve accurate fault prediction. In this paper, an intelligent algorithm framework is proposed, which combines the high efficiency of decision tree with the advantage of diversity and robustness, to enhance the precision of diagnosis and forecast. The simulation experiment is tested based on the actual operation data of a certain type of ship power system. The results show that the fault diagnosis accuracy of the model reaches 98.6%, and the recall rate of fault prediction is 96.2%. Compared with traditional methods, the model reduces the diagnosis time and false alarm rate by 27% and 15% respectively. The research results verify the practicality and efficiency of the proposed model in complex power systems. The research in this paper provides a feasible technical path for the intelligent operation and maintenance of ship power systems, which has high application value and promotion potential.