Fault Diagnosis of Locomotive Wheel-bearing Based on Wavelet Packet and MCA

被引：0

作者：

Yang, Wei-feng ^{[1
]}

He, De-qiang ^{[2
]}

Chen, Tao ^{[3
]}

Yao, Zi-kai ^{[2
]}

机构：

[1] Zhuzhou CRRC Times Elect Co LTD, Zhuzhou 412001, Peoples R China

[2] Guangxi Univ, Sch Mech Engn, Nanning 530004, Peoples R China

[3] Nanning CRRC Aluminum Precis Proc Co LTD, Nanning 530031, Peoples R China

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONIC ENGINEERING (EEE 2019) | 2019年 / 185卷

关键词：

Locomotive bearing; Fault diagnosis; Wavelet packet; Morphological component analysis;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Fault diagnosis of locomotive wheel-bearing is directly related to the locomotive performance and the safe operation of train. Owing to the fault signal of locomotive wheel-bearing being difficult to separate, the fault diagnosis method was proposed, which based on wavelet packets and morphological component analysis combined with the vibration signal characteristics of locomotive wheel-bearing. The simulation results show that the fault diagnosis of the locomotive wheel-bearing under low signal-to-noise ratio (SNR) case is achieved by wavelet packet and morphological component analysis. It provides a theoretical basis for the fault diagnosis and condition monitoring for the locomotive wheel-bearing.

引用

页码：158 / 163

页数：6

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