De-Noising Method of Acoustic Emission Signal for Rolling Bearing Based on Adaptive Wavelet Correlation Analysis

Abstract:

In acoustic emission (AE) detection technique, to avoid the serious noise disturbance in the fault diagnosis of rotary machine, a de-noising method based on adaptive wavelet correlation analysis to be applied to the AE signal is proposed. First, AE signals are decomposed by dyadic wavelet transform and at the same time the AE signal is divided into available coefficients and noise coefficients. Secondly, the available coefficients are reconstructed to restore the original real signal after de-noising process. Finally, the de-noising threshold is set by adaptive threshold method based on wavelet entropy. On the simulation of AE signal and the bearing fault measured AE signal using wavelet entropy correlation de-noising method, the traditional wavelet de-noising method and the traditional lifting wavelet de-noising method three kinds of de-noising methods are compared, the results show that the wavelet entropy correlation de-noising method can greatly improve the rolling bearing AE signal de-noising effect.

• Periodical: Applied Mechanics and Materials (Volume 273)
• Pages: 188-192
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.273.188
• Online since: January 2013
• Authors: Xin Li, Xue Jun Li, Guang Bin Wang
• Keywords: AE Signal De-Noising, Correlation Analysis, Rolling Bearing, Wavelet Entropy

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