关键词: 随机森林, 电机, 故障诊断, 特征选择, 调整兰德指数, 粒子群优化

Abstract: Aiming at the problem of low recognition accuracy of three-phase motor,an intelligent fault diagnosis method was studied.Taking the three-phase motor vibration data as the research object,combining particle swarm optimization algorithm with random forest algorithm,an optimized random forest algorithm model was established to identify the motor fault state.A fusion K-means clustering algorithm and random forest algorithm were combined, a sensitive feature extraction algorithm was proposed to extract the fault-sensitive features.The eight operating states of the motor are experimentally verified.The experimental results show that the method can identify the motor fault state accurately and efficiently .