经验小波在机车轴承故障诊断中的应用  

Locomotive Bearing Fault Diagnosis Using Empirical Wavelet Transform

作　　者：许人杰 XU Renjie(China Railway Materials Railway Equipment Co., Ltd., Beijing 100036, China)

机构地区：[1]中铁物总铁路装备物资有限公司,北京100036

出　　处：《机车电传动》2019年第5期46-49,共4页Electric Drive for Locomotives

摘　　要：针对机车轴承故障诊断中故障特征提取的难题,将经验小波变换(EWT)引入机车轴承振动信号分析。经验小波通过构造紧支撑自适应滤波器将信号分解为多个固有模态分量,能有效抑制模态混叠。针对轴承振动特征对经验小波变换进行改进,提出了首先利用改进经验小波变换分解机车轴承振动信号,然后以峭度为指标筛选敏感分量,进而对敏感分量进行希尔伯特包络解调提取轴承故障特征的诊断方法。机车运行试验表明,文章所提出的方法划分机车轴承振动信号频带合理,能有效提取轴承故障特征频率,准确诊断各种类型的轴承故障。Aiming at the difficult problem of fault feature extraction in locomotive bearing fault diagnosis, the empirical wavelet transform (EWT) was introduced into locomotive bearing vibration signal analysis. Empirical wavelet transform decomposed the signal into multiple intrinsic modal components by constructing compactly supported adaptive filters, which could effectively suppress modal aliasing. In this paper, the empirical wavelet transform (EWT) was improved for bearing vibration characteristics. Firstly, the EWT was used to decompose the vibration signals of locomotive bearings, then the sensitive components were screened by kurtosis, and then the Hilbert envelope demodulation was used to extract the fault features of bearings. The locomotive operation experiment showed that the proposed method is reasonable in dividing the frequency band of vibration signal of locomotive bearing, and can effectively extract the characteristic frequency of bearing fault and accurately diagnose various types of bearing fault.

关 键 词：经验小波变换 故障诊断 机车 轴承 

分 类 号：U269.322[机械工程—车辆工程] U260.331.2[交通运输工程—载运工具运用工程]