A Diagnosis Method Based on Wavelet Coefficient Scale Relativity Correlation Dimension for Fault

Author

Gu, Jun-jie ; Tian, Jin ; Peng, Xue-zhi ; Qin, Zhi-ming

Author_Institution

North China Electr. Power Univ., Baoding

Volume

2

fYear

2007

Firstpage

1076

Lastpage

1080

Abstract

This paper introduces correlation dimension as a tool to describing machinery condition. A threshold filtering algorithm based on the region relativity of the wavelet coefficients for reducing noise is introduced. Utilizing the above method, the vibration signals of the fan under different working conditions are analyzed. The result shows that the characteristics of the signal could be preserved completely. The correlation dimension is able to identify the conditions of the fan with faults compared with the normal condition, thereby providing an effective technology for condition monitoring and fault diagnosis of mechanical equipment.

Keywords

condition monitoring; fans; fault diagnosis; mechanical engineering computing; vibrations; wavelet transforms; condition monitoring; fault diagnosis method; machinery condition; mechanical equipment; threshold filtering algorithm; vibration signals; wavelet coefficient scale relativity correlation dimension; Condition monitoring; Cybernetics; Fault diagnosis; Frequency estimation; Machine learning; Noise reduction; Signal analysis; Vibrations; Wavelet coefficients; White noise; Correlation dimension; Fan; Fault diagnosis; Scale relativity; Wavelet Coefficient;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2007 International Conference on

Print_ISBN

978-1-4244-0973-0

Electronic_ISBN

978-1-4244-0973-0

Type

conf

DOI

10.1109/ICMLC.2007.4370303

Filename

4370303