Fault Prediction and Maintenance Strategy of Motor Drive System Based on Data Mining

This paper discusses the fault prediction and maintenance strategy of motor transmission system based on data mining technology. Due to the complexity and changeable operating environment of motor transmission system, its frequent failures pose challenges to industrial production efficiency and cost control. Traditional maintenance strategies can't effectively prevent sudden failures, but data mining technology provides a new solution. In this paper, the early warning and accurate maintenance are realized by monitoring and analyzing the motor operation data in real time, mining the fault symptoms, thus improving the reliability and maintenance efficiency of the system. In research, deep learning techniques have been used for motor fault prediction due to their powerful feature extraction and nonlinear modeling capabilities, especially long short-term memory network (LSTM) models, which are suitable for processing time-series dependent data and capturing dynamic features of fault development. Through the experimental verification, the LSTM model shows 85% precision, 83% accuracy, 79% recall rate and 81% F1 score in fault prediction, showing excellent performance. In addition, this paper also puts forward the application of personalized maintenance plan and cluster analysis based on the prediction results in maintenance, so as to realize the intelligent maintenance strategy of motor transmission system and effectively improve the reliability and availability of motor system.