The research of steel tube defect signal denoising based on PSO wavelet threshold

Author

Zhou, Yilin ; Zhang, Yuanqi

Author_Institution

Inst. of Autom. & Electron. Eng., Qingdao Univ. of Sci. & Technol., Qingdao, China

fYear

2011

fDate

16-18 Dec. 2011

Firstpage

2219

Lastpage

2222

Abstract

In order to get a more chear steel tube defects echo signal, we put to use the wavelet denoising methods. After studying the characteristics of Particle Swarm Optimization algorithm, Particle Swarm Optimization is used in wavelet domain to optimize the thresholds, and garrote function is used to quantize wavelet decomposition coefficients, which can overcome the shortcoming of discontinuity of hard-threshold and decrease the permanent bias of soft-threshold. The simulation results show that denoising signal is more close to the original signal through Particle Swarm Optimizing wavelet threshold, no matter the mean-square error or signal noise ratio.

Keywords

acoustic signal processing; flaw detection; mean square error methods; particle swarm optimisation; pipes; production engineering computing; quantisation (signal); signal denoising; steel; wavelet transforms; PSO wavelet threshold; garrote function; mean-square error; particle swarm optimization algorithm; signal noise ratio; soft-threshold permanent bias; steel tube defect echo signal denoising; wavelet decomposition coefficients; wavelet denoising methods; Noise; Noise reduction; Particle swarm optimization; Wavelet coefficients; Wavelet domain; Particle Swarm Optimization; the signal to noise ratio; wavelet threshold;

fLanguage

English

Publisher

ieee

Conference_Titel

Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4577-1700-0

Type

conf

DOI

10.1109/TMEE.2011.6199660

Filename

6199660