Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings
Joint Authors
Xiong, Jianbin
Liang, Qiong
Zhang, Qinghua
Zhu, Hongbin
Li, Haiying
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-23
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection.
In this paper, a parameter optimization algorithm for the SVM is proposed based on multi-genetic algorithm.
The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model.
The experimental results demonstrate that by combining the genetic algorithm and SVM algorithm, fault diagnosis can be effectively realized for bearings of rotating machinery.
American Psychological Association (APA)
Xiong, Jianbin& Zhang, Qinghua& Liang, Qiong& Zhu, Hongbin& Li, Haiying. 2018. Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings. Shock and Vibration،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1215164
Modern Language Association (MLA)
Xiong, Jianbin…[et al.]. Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings. Shock and Vibration No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1215164
American Medical Association (AMA)
Xiong, Jianbin& Zhang, Qinghua& Liang, Qiong& Zhu, Hongbin& Li, Haiying. Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1215164
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215164