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论文类型：期刊论文

发表时间：2014-01-01

发表刊物：MEASUREMENT

收录刊物：SCIE、EI

卷号：47

期号：1

页面范围：669-675

ISSN号：0263-2241

关键字：Roller bearing; Fault diagnosis; Hierarchical entropy; SVM; PSO

摘要：Targeting the non-linear dynamic characteristics of roller bearing faulty signals, a fault feature extraction method based on hierarchical entropy (HE) is proposed in this paper. SampEns of 8 hierarchical decomposition nodes (e. g. HE at scale 4) are calculated to serve as fault feature vectors, which takes into account not only the low frequency components but also high frequency components of the bearing vibration signals. HE can extract more faulty information than multi-scale entropy (MSE) which considers only the low frequency components. After extracting HE as feature vectors, a multi-class support vector machine (SVM) is trained to achieve a prediction model by using particle swarm optimization (PSO) to seek the optimal parameters of SVM, and then ten different bearing conditions are identified through the obtained SVM model. The experimental results indicate that HE can depict the characteristics of the bearing vibration signal more accurately and more completely than MSE, and the proposed approach based on HE can identify various bearing conditions effectively and accurately and is superior to that based on MSE. (C) 2013 Elsevier Ltd. All rights reserved.