Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-14
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model.
Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result.
For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal.
The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm.
The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters.
The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result.
The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.
American Psychological Association (APA)
Zuo, Shilun& Liao, Zhiqiang. 2020. Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1193880
Modern Language Association (MLA)
Zuo, Shilun& Liao, Zhiqiang. Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1193880
American Medical Association (AMA)
Zuo, Shilun& Liao, Zhiqiang. Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1193880
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193880