Research on The Roller Bearing Fault Diagnosis Based on Morphological Wavelet and LSSVM Algorithm

被引：0

作者：

Han, Xing ^{[1
]}

Xiong, Jingqi ^{[1
]}

Sun, Rui ^{[1
]}

Wang, Lili ^{[1
]}

Qin, Xiaopin ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Mechatron Engn, Chengdu 611731, Peoples R China

来源：

PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING (QR2MSE), VOLS I-IV | 2013年

关键词：

roller bearing; morphological wavelet; LSSVM; fault diagnosis;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In response to the roller bearing fault, the method of fault recognition of rolling bearings based on Morphological Wavelet and Least Squares Support Vector Machine was presented. Morphological Wavelet was applied to signal preprocessing, energy feature method to signal extraction. And then, we carry out the recognition of fault types using Least Square Support Vector Machine algorithm. After testing and simulation, the results showed that the fault diagnosis method based on Morphological Wavelet and Least Square Support Vector Machine can diagnose typical faults of the roller bearing effectively.

引用

页码：1888 / 1892

页数：5

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