Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm
Joint Authors
Yang, Wen-An
Xiao, Maohua
Zhou, Wei
Guo, Yu
Liao, Wenhe
Shen, Gang
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-10
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns.
Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems.
Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems.
This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA) for fault diagnosis of rolling element bearings.
The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA.
The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA) can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings.
Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data.
The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings.
American Psychological Association (APA)
Yang, Wen-An& Xiao, Maohua& Zhou, Wei& Guo, Yu& Liao, Wenhe& Shen, Gang. 2016. Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm. Shock and Vibration،Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1119865
Modern Language Association (MLA)
Yang, Wen-An…[et al.]. Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm. Shock and Vibration No. 2016 (2016), pp.1-15.
https://search.emarefa.net/detail/BIM-1119865
American Medical Association (AMA)
Yang, Wen-An& Xiao, Maohua& Zhou, Wei& Guo, Yu& Liao, Wenhe& Shen, Gang. Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1119865
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1119865