基于PWM开关高频振荡的变频电机轴承故障诊断  

Fault Diagnosis of Inverter-Fed Machine Bearing Based on High-Frequency Oscillation of PWM Switch

作　　者：韩佳良 顾奕 刘静宇 李豪 吴琦 陈逸凡 HAN Jialiang;GU Yi;LIU Jingyu;LI Hao;WU Qi;CHEN Yifan(School of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Pudong Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 200122,China;Fengxian Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 201400,China)

机构地区：[1]上海电力大学电气工程学院,上海200090 [2]国网上海市电力公司浦东供电公司,上海200122 [3]国网上海市电力公司奉贤供电公司,上海201400

出　　处：《上海电力大学学报》2024年第5期484-490,共7页Journal of Shanghai University of Electric Power

基　　金：国家自然科学基金(51907116);上海市自然科学基金(22ZR1425400)。

摘　　要：为了解决传统变频电机轴承故障诊断方法易受到低频噪声和基频电流干扰的问题,提出了一种利用脉冲宽度调制(PWM)开关高频振荡进行变频电机轴承故障诊断的新方法。首先,分析了变频电机轴承故障诊断的机理以及轴承电流的产生机理和流通路径,并用仿真验证了PWM开关dv/dt轴承电流对轴承故障状态敏感;然后,利用PWM开关dv/dt轴承电流是地线电流的一个分量,将地线电流中的高频振荡信号作为故障诊断特征变量和数据源;最后,设计了一种用于电机轴承故障诊断的轻量级一维卷积神经网络模型,用于特征提取和故障分类。实验结果表明,该方法的准确率达到了96.63%,能够准确诊断变频电机轴承故障。In order to solve the problem that the traditional fault diagnosis method of variable frequency motor bearing is susceptible to low frequency noise and fundamental frequency current interference,a new method utilizing high-frequency oscillations from pulse width modulation(PWM)switches is proposed for diagnosing bearing faults ininverter-fed machine.Firstly,the mechanisms of bearing fault diagnosis in inverter-fed machine and the generation pathways of bearing currents are analyzed.Then,due to the sensitivity of PWM switch dv/dt bearing current to bearing fault states,the measured PWM switch dv/dt bearing current,which is a component of the ground current,is used as a diagnostic variable and data source.Then,the measurement of PWM switch ground current does not require invasive sensors compared to dv/dt bearing current.Finally,the lightweight one-dimensional convolutional neural network is designed for motor bearing fault diagnosis,serving the purposes of feature extraction and fault classification.Experimental results demonstrate an accuracy rate of 96.63%,confirming the effectiveness of this method.

关 键 词：变频电机 开关振荡 卷积神经网络 轴承故障诊断 

分 类 号：TM215[一般工业技术—材料科学与工程]