Paper Title:
Extracting of Weak Periodic Impact Feature Based on Mean Filtering and Wavelet Transform
  Abstract

Based on mean filtering with good denoising capability for white gaussian noise and wavelet transform with high frequency denoising and singularity detection capabilities, a new method combining mean filtering and wavelet transform is proposed for extracting weak periodic impact signal in heavy noise background. Three different thresholds of wavelet transform are used to extract feature. The simulation results show that the wavelet filtering with improved threshold has the best effect. The SNR(Signal to Noise Ratio) are greatly improved and RMSE(Root Mean Square Error) are greatly reduced.This method has an excellent effect on extracting weak periodic impact feature and has very strong practicability.

  Info
Periodical
Advanced Materials Research (Volumes 383-390)
Chapter
Chapter 1: Computer-Aided Manufacturing
Edited by
Wu Fan
Pages
408-413
DOI
10.4028/www.scientific.net/AMR.383-390.408
Citation
F. Du, T. B. Ma, "Extracting of Weak Periodic Impact Feature Based on Mean Filtering and Wavelet Transform", Advanced Materials Research, Vols. 383-390, pp. 408-413, 2012
Online since
November 2011
Authors
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Price
$32.00
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