A signal processing approach for enhanced Acoustic Emission data analysis in high activity systems: application to Organic Matrix Composites

International audienceIn Acoustic Emission (AE) applications, the processing of continuous signals resulting from high AE rates or the superimposition of transients emitted from different emission sources, is a major problem. In complex systems like Organic Matrix Composites (OMC) fatigue tests, a high AE activity is produced due to the emergence of several emission sources. Such a kind of tests often involves a lot of noise that corrupts the original signal. Conventional threshold-based techniques are highly influenced by the noise level leading to erroneous hits detection. A suitable denoising method is thus necessary to process the signal before performing the hits detection and separation.This work deals with the processing of continuous AE signals obtained from Carbon Fiber Reinforced Plastics (CFRP) specimens under complex loading. When the size of each acquisition is large due to the sampling rate (generally from 2 to 5 MS/s), the signal is divided into short segments. The Discrete Wavelet Transform (DWT) is then used for signal denoising. An adjustment of its input parameters is achieved in order to improve the denoising process. A hit determination is thereafter performed in order to localize potential hits contained in each signal segment. By comparing the result obtained using the proposed approach to that of the usual threshold-based technique, we remark that the problem of erroneous hits is overcome. The performance of the proposed approach as well as the sensitivity to the denoising parameters are evaluated by studying the impact of errors in hit detection on feature extraction and damage assessment based on pattern recognition algorithms. The proposed approach ensures a better identification of natural clusters in AEs and improves the interpretation of damage mechanisms