Research on the AE Signal De-noising Based on K-Means Clustering and the Wavelet Transform

Abstract

The noise in the acoustic emission (AE) signal must be removed to identify the mode of AE signal accurately. The Wavelet threshold de-noising method shows some unique advantages. Based on the threshold selection risky problem, K-means clustering method was used to classify the high-frequency coefficients by the wavelet decomposition to determine the removal threshold for the wavelet coefficients corresponding to the noise, and achieve the de-noising purpose. Hard-threshold method and soft-threshold method were applied to AE signal through the wavelet threshold de-noising. The thresholds generated by K-means clustering approach and the Donoho method improved were respectively used as the threshold for the de-noising of the wavelet coefficients. The experimental results show that the method proposed is superior to the Donoho method improved in the three indicators of signal to noise ratio, root mean square error.

Cite this paper

@inproceedings{Zhang2015ResearchOT, title={Research on the AE Signal De-noising Based on K-Means Clustering and the Wavelet Transform}, author={Zhengben Zhang and Chongke Wang}, year={2015} }