Fault Diagnosis for Reducer via Improved LMD and SVM-RFE-MRMR
Joint Authors
Zhang, Xiaoguang
Zhao, Zhike
Song, Zhenyue
Li, Dandan
Zhang, Wei
Chen, Yingying
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-15
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The vibration signals are usually characterized by nonstationary, nonlinearity, and high frequency shocks, and the redundant features degrade the performance of fault diagnosis methods.
To deal with the problem, a novel fault diagnosis approach for rotating machinery is presented by combining improved local mean decomposition (LMD) with support vector machine–recursive feature elimination with minimum redundancy maximum relevance (SVM-RFE-MRMR).
Firstly, an improved LMD method is developed to decompose vibration signals into a subset of amplitude modulation/frequency modulation (AM-FM) product functions (PFs).
Then, time and frequency domain features are extracted from the selected PFs, and the complicated faults can be thus identified efficiently.
Due to degradation of fault diagnosis methods resulting from redundant features, a novel feature selection method combining SVM-RFE with MRMR is proposed to select salient features, improving the performance of fault diagnosis approach.
Experimental results on reducer platform demonstrate that the proposed method is capable of revealing the relations between the features and faults and providing insights into fault mechanism.
American Psychological Association (APA)
Zhang, Xiaoguang& Song, Zhenyue& Li, Dandan& Zhang, Wei& Zhao, Zhike& Chen, Yingying. 2018. Fault Diagnosis for Reducer via Improved LMD and SVM-RFE-MRMR. Shock and Vibration،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1215262
Modern Language Association (MLA)
Zhang, Xiaoguang…[et al.]. Fault Diagnosis for Reducer via Improved LMD and SVM-RFE-MRMR. Shock and Vibration No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1215262
American Medical Association (AMA)
Zhang, Xiaoguang& Song, Zhenyue& Li, Dandan& Zhang, Wei& Zhao, Zhike& Chen, Yingying. Fault Diagnosis for Reducer via Improved LMD and SVM-RFE-MRMR. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1215262
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215262