Rolling bearing fault diagnosis based on empirical mode decomposition and support vector machine

被引：10

作者：

Xu K. ^{[1
,2
]}

Chen Z.-H. ^{[1
,2
]}

Zhang C.-B. ^{[1
,2
]}

Dong G.-Z. ^{[1
]}

机构：

[1] School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, Anhui

[2] Coordinated Innovation Center for Health Operation of Rail Transit, Zhuhai, 519070, Guangdong

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Empirical mode decomposition; Fault diagnosis; Particle swarm optimization; Rolling bearing; Support vectormachine;

D O I：

10.7641/CTA.2018.80257

中图分类号：

学科分类号：

摘要：

In this paper, an adaptive waveform matching method is proposed to improve the end effect of empirical mode decomposition(EMD).Then a two-phase fault diagnosis method for rolling bearing is presented based on improved EMD and Particle Swarm Optimization(PSO)optimized support vector machine(SVM).In the of fline phase, the typical normal and fault vibration signals are decomposed by IEMD and energy information is extracted as the feature.A PSO-SVM model is trained and saved as diagnostic model.In the online phase, the real-time vibration signal is decomposed by IEMD and the feature is extracted.The model trained in of fline phase executes diagnostic process and output the diagnosis results.The method is veri fied using Case Western bearing datasets.The experimental results show the effectiveness of the method in fault diagnosis of rolling bearing. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：915 / 922

页数：7

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