ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE

ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE

According to characteristics of the bearing signal,the second-order cyclic demodulation information was introduced into machine learning,and a multi-domain kernel extreme learning machine（MKELM） based on the combination of cyclic autocorrelation（CAF） frequency domain features and time domain feature...

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Bibliographic Details
Main Authors: WANG XiaoHui, WANG GuangBing, XIANG JiaWei, HUANG Zhen, SUI GuangZhou
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Strength 2020-01-01
Series:Jixie qiangdu
Subjects:
Bearing;Cyclic autocorrelation;Kernel extreme learning machine;Pattern recognition
Online Access:http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.005

Internet

http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.005

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