基于小波变换的改进LMS算法在异步电动机轴承故障诊断中应用

基于电机定子电流信号分析方法的异步电动机轴承故障检测中,计及实际电动机供电电压谐波和三相电压不平衡等外部因素的情况下,如何实现轴承故障的可靠检测一直是电动机故障检测领域的难题.对传统的定子电流频谱分析方法进行了深入研究,讨论了传统最小均方算法(LMS)自适应滤渡方法在信号处理中的不足.在此基础上,提出了将小渡分析、连续细化傅里叶变换和改进LMS自适应滤波方法有机结合的异步电动机轴承故障检测新方法.该方法能够正确判断轴承故障特征频率分量,从而提高异步电动机轴承故障诊断效果,实现轴承故障的可靠检测.实验结果表明了该方法的有效性.

Application of modified LMS algorithm to induction motor bearing fault diagnosis

It is difficult to realize reliable detection in induction motor bearing fault diagnosis by MCSA(Motor stator Current Signature Analysis) when the voltage harmonics of power supply are taken into account and the three -phase voltages are unbalanced. The conventional spectrum analytical method of stator current is studied and the weakness of conventional LMS (Least -Mean -Square) algorithm in real -time signal processing is discussed,based on which and by the perfect combination of the wavelet transform,continuous subdivision Fourier transform and modified LMS self -adaptive filter algorithm,a scheme to reliably detect induction motor bearing fault is proposed. It can correctly identify the characteristic frequency of bearing fault and greatly improve the diagnosis effectiveness. Experimental results show its feasibility.