Signal-Denoising Based on DT-CWT and Its Extraction to Gear Weak Fault Information

Author

Xiangui Liu

Author_Institution

Dept. of Mech. Eng., Nanchang Institue of Technol.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

5838

Lastpage

5841

Abstract

It is introduced a signal-denoising method using local adaptive algorithm based on complex wavelet to extract weak fault information in gear. It takes advantage of shift invariance of dual-tree complex wavelet transform, non-Gaussian probability distribution for the wavelet coefficients and the statistical dependencies between a coefficient and its parent. So it can obtain higher SNR (signal-to-noise ratio) than common methods based on discrete wavelet transform. Some actual gearbox vibration signals are analyzed, and the results show the effectiveness of the proposed method in monitoring and diagnosis of machine conditions, with the capability of early fault detection by extracting periodic impulses from gearbox vibration signals

Keywords

Gaussian distribution; condition monitoring; discrete wavelet transforms; fault diagnosis; gears; mechanical engineering computing; signal denoising; trees (mathematics); vibrations; discrete wavelet transform; dual-tree complex wavelet transform; fault detection; gear fault diagnosis; gear weak fault information extraction; gearbox vibration signals; local adaptive algorithm; machine condition monitoring; nonGaussian probability distribution; signal-denoising; signal-to-noise ratio; Adaptive algorithm; Condition monitoring; Data mining; Discrete wavelet transforms; Fault diagnosis; Gears; Probability distribution; Signal analysis; Signal to noise ratio; Wavelet coefficients; Dual-tree complex wavelet transform; Early fault detection; Gear fault diagnosis; Signal-denoising;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714196

Filename

1714196