文章基本信息

标题：Railway rolling bearing fault diagnosis based on multi-scale intrinsic mode function permutation entropy and extreme learning machine classifier
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作者：Dechen Yao ; Jianwei Yang ; Yongliang Bai 等
期刊名称：Advances in Mechanical Engineering
印刷版ISSN：1687-8140
电子版ISSN：1687-8140
出版年度：2016
卷号：8
期号：10
DOI：10.1177/1687814016676157
语种：English
出版社：Sage Publications Ltd.
摘要：The application of the multi-scale intrinsic mode function permutation entropy and extreme learning machine classifiers in railway rolling bearing fault diagnosis is here proposed in this article. The original signal is first denoised using wavelet de-noising as a pre-filter, which improves the subsequent decomposition into a number of intrinsic mode functions using ensemble empirical mode decompose. Second, the multi-scale intrinsic mode function permutation entropy is extracted as feature parameters. Finally, the extracted features are entered into extreme learning machine for an automated fault diagnosis procedure. Case studies have been carried out to evaluate the validity of the approach. The results demonstrate its effectiveness for diagnosis of faults in railway rolling bearings.
关键词：Fault diagnosis; railway rolling bearing; ensemble empirical mode decompose; multi-scale permutation entropy; extreme learning machine