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.

• Periodical: Advanced Materials Research (Volumes 383-390)
• Pages: 408-413
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.383-390.408
• Online since: November 2011
• Authors: Fei Du, Tian Bing Ma
• Keywords: Feature Extracting, Mean Filtering, Wavelet Transform (WT), Weak Periodic Impact

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