Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings
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- 作者：Meng Luoa ; Chaoshun Lia ; csli@hust.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Xiaoyuan Zhangb ; Ruhai Lia ; Xueli Anc
- 关键词：Extreme learning machine ; Gravitational search algorithm ; Parameter optimization ; Feature selection ; Ensemble empirical mode decomposition ; Fault diagnosis
- 刊名：ISA Transactions
- 出版年：2016
- 出版时间：November 2016
- 年：2016
- 卷：65
- 期：Complete
- 页码：556-566
- 全文大小：1133 K
文摘
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A novel fault diagnosis method based on compound feature selection and parameter optimization of ELM is presented.
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Compound features which consist of time-frequency features, EEMD energy features and EEMD singular features are extracted.
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The compound feature set and parameters of ELM are optimized simultaneously by using a hybrid GSA.
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Results show that HGSA-ELM achieves high accuracy compared with the original ELM and methods in literatures.