Study on the method of motor short-circuit fault based on the improved random forest

This study presents an improved random forest algorithm for motor short-circuit fault diagnosis. The classification accuracy of the model is significantly improved by data preprocessing, time and frequency domain feature extraction, and parameter optimization. The experimental results show that the improved random forest algorithm performs well in the fault classification task and has obvious advantages over the traditional methods. This study provides an effective solution for motor fault diagnosis and provides a reference for further research in related fields.