A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm
Joint Authors
Zhang, Chao
Fan, Yerui
Xue, Yu
Wang, Jianguo
Gu, Fengshou
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-20
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In this paper, a novel model for fault detection of rolling bearing is proposed.
It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO).
The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property.
Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement.
Finally, the proposed model is evaluated through experiments and comparative studies.
The results prove its effectiveness in detecting and classifying bearing faults.
American Psychological Association (APA)
Fan, Yerui& Zhang, Chao& Xue, Yu& Wang, Jianguo& Gu, Fengshou. 2020. A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm. Shock and Vibration،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1213665
Modern Language Association (MLA)
Fan, Yerui…[et al.]. A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm. Shock and Vibration No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1213665
American Medical Association (AMA)
Fan, Yerui& Zhang, Chao& Xue, Yu& Wang, Jianguo& Gu, Fengshou. A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1213665
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1213665