Indian Journal of Science and Technology

Abstract

Background/Objectives: The article considers different filtering methods for acoustic emission data. The discussion of data filtering algorithms is aimed at improving the noise immunity of the acoustic emission system. Method: Noise filtering methods (frequency filtering, time series disorder detection, etc.) are examined for AE testing. Findings: One problem of the acoustic emission testing is a great number of noises affecting the diagnosis results. Electric noises, electromagnetic interference, background acoustic noise, rubbing noises are far from the full list of noises available during measurements. At the high level of noises, the operator has to increase the recording threshold of the acoustic emission impulses through reducing the testing sensitivity at the risk of missing a dangerous defect. Lack of the data filtering can result in an incorrect localization and erroneous definition of the danger level of acoustic emission source. Different noise types have been investigated and noise classification method according to the filtering complexity has been suggested to solve effectively the problem of the acoustic emission test data filtering. Wavelet-filtering efficiency for white stochastic noise removal has been shown. Algorithm for impulse noise filtering has been described. Improvements: The offered data processing approaches allow enhancing the sensitivity of AE testing especially for the operating structures. 

Keywords: Acoustic Emission (AE) of Operating Structures, Signal Processing, Time Series Analysis