Wavelet Based Clustering of Acoustic Emission Hits to Characterize Damage Mechanisms in Composites

Abstract : Clustering data is an important topic in acoustic emission (AE) health monitoring. Identification of damage mechanisms in composites via AE technique requires an automatic process of classification. In this work, we propose a novel and simple supervised method to discriminate AE signals produced by fracture mechanisms in polymer composites. The novelty of this work is to propose new pertinent descriptors offered by using the continuous wavelet transform, where signals of learning are decomposed and the corresponding wavelet coefficients are calculated. In addition, the entropy criterion is applied to select the most correlated wavelets associated to each failure mechanism. This process allows to establish a filter in the form of vectors for each class of signals and descriptors denote the reconstruction errors calculated by involving the filter associated to each damage mechanism. The k-means algorithm is executed to calculate the center of each class. The technique is applied to AE signals recorded from specific mechanical tests to demonstrate the performance of the proposed descriptors.