Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Joint Authors

Zuo, Shilun
Liao, Zhiqiang

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

Civil Engineering

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