异步电机轴承故障的诊断方法研究  

Research on Diagnosis Method of Bearing Fault of Induction Motor

作　　者：王宇清 杨莘苑 张折桂 阚超豪[1] WANG Yuqing;YANG Xinyuan;ZHANG Zhegui;KAN Chaohao(School of Electrical and Automation Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学电气与自动化工程学院,合肥230009

出　　处：《微特电机》2024年第4期33-37,共5页Small & Special Electrical Machines

基　　金：安徽省大学生创新创业训练计划项目(S202310359049)。

摘　　要：提出了基于Park矢量模平方函数及其数据处理的轴承故障诊断方法,计算电机轴承三相电流信号的Park矢量模平方函数,再经小波包分解分别至四层和五层,得到故障特征频率对应频带,并取其RMS值,以其RMS值经对数处理放大差异后与正常电机进行比较从而进行轴承故障诊断。通过求解小波包分解子频带所对应节点系数的RMS值,从而判断电机轴承故障,并且搭建了样机试验平台,测试结果验证了该方法的有效性。A bearing fault diagnosis method was proposed based on the Park vector modular square function and its data processing.The Park vector modular square function of the three-phase current signal of the motor bearing was calculated,and the Park vector modular square function was decomposed into the fourth and fifth layers by wavelet packet to obtain the frequency band corresponding to the fault characteristic frequency and took its RMS value.The bearing fault diagnosis was carried out by comparing the RMS value with the normal motor after logarithmic processing.By solving the RMS value of the node coefficient corresponding to the wavelet packet decomposition subband,the motor fault was judged,and the prototype test platform was built.The test results verified the effectiveness of the method.

关 键 词：异步电机 轴承故障 PARK矢量 小波包分解 均方根值 

分 类 号：TM301.3[电气工程—电机]