科技创新与应用2025，Vol.15Issue(14)：26-30,5.DOI:10.19981/j.CN23-1581/G3.2025.14.006
基于RBF神经网络的永磁同步电机匝间短路故障诊断方法
摘要
Abstract
Inter-turn short circuit faults are one of the most common and serious faults that occur in permanent magnet synchronous motors(PMSM).Motor fault diagnosis technology is an important means to improve motor reliability and reduce fault losses.Therefore,this paper proposes a method based on RBF neural network to diagnose inter-turn short circuit faults in permanent magnet synchronous motors.First,a finite element model of inter-turn short circuit fault of permanent magnet synchronous motor is established.The motor winding is divided into multiple sub-windings,and the two ends of the sub-windings are connected in parallel to simulate inter-turn short circuit faults.Secondly,the established finite element model is used to simulate the motor performance under different fault degrees.The paper analyzes and extracts fault characteristics from motor torque,phase voltage,and phase current.Finally,a fault diagnosis system is established using RBF neural network.It has been verified that the proposed fault diagnosis method can diagnose different degrees of inter-turn short circuits.关键词
永磁同步电机/匝间短路故障/故障程度/故障特征提取/RBF神经网络Key words
permanent magnet synchronous motor/inter-turn short circuit fault/fault degree/fault feature extraction/RBF neural network分类
计算机与自动化引用本文复制引用
李欣蔚..基于RBF神经网络的永磁同步电机匝间短路故障诊断方法[J].科技创新与应用,2025,15(14):26-30,5.基金项目
河南财经政法大学2022年度校级本科教学工程专题项目(无编号) （无编号）
