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Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement
Joint Authors
Han, Long
Li, Chengwei
Shen, Liqun
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-30
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Due to the powerful ability of EEMD algorithm in noising, it is usually applied to feature extraction of fault signal of rolling bearing.
But the selective correctness of sensitive IMF after decomposition can directly influence the correctness of feature extraction of fault signal.
In order to solve the problem, the paper firstly proposes a new method on selecting sensitive IMF based on Cloud Similarity Measurement.
By comparing this method in simulation experiment with the traditional mutual information method, it is obvious that the proposed method has overcome the misjudgment in the traditional method and it has higher accuracy, by factually collecting the normal, damage, and fracture fault AE signal of the inner ring of rolling bearing as samples, which will be decomposed by EEMD algorithm in the experiments.
It uses Cloud Similarity Measurement to select sensitive IMF which can reflect the fault features.
Finally, it sets the Multivariate Multiscale Entropy (MME) of sensitive IMF as the eigenvalue of original signal; then it is classified by the SVM to determine the fault types exactly.
The results of the experiments show that the selected sensitive IMF based on Cloud Similarity Measurement is effective; it can help to improve the accuracy of the fault diagnosis and feature extraction.
American Psychological Association (APA)
Han, Long& Li, Chengwei& Shen, Liqun. 2015. Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement. Shock and Vibration،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1078330
Modern Language Association (MLA)
Han, Long…[et al.]. Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement. Shock and Vibration No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1078330
American Medical Association (AMA)
Han, Long& Li, Chengwei& Shen, Liqun. Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement. Shock and Vibration. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1078330
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078330