Contribution of Functional Approach to the Classification and the Identification of Acoustic Emission Source Mechanisms

Abstract

In a context of nuclear Reactivity Initiated Accident, we describe acoustic emission signals, for which a problem of classification is open. As classical approaches with a reduced number of variables do not give satisfactory discrimination, we propose to use the envelopes of the received signals. We perform a k-means clustering and discuss the first results of this approach.

Clustering acoustic emission signals by mixing two stages dimension reduction and nonparametric approaches

“…A detailed presentation of the part of the preprocessing consisting in the estimation of x and the detection of the hits is beyond the scope of this article. For more details, the reader can refer to our works [Pantera and Traore, 2015, Traore et al, 2017d, Traore et al, 2017c, Traore et al, 2017b, Traore et al, 2017a. We note that here, a default treatment of y based on a spectral subtraction denoising and a hit detection using a moving variance algorithm (Figure 3) have been chosen.…”

Functional clustering via multivariate clustering

“…In Jacques and Preda (2014) the functional clustering techniques are classified into four categories: raw data methods, which consist on considering the functional data set as a multivariate one and apply there the clustering techniques studied for multivariate data (Boullé (2012)); the filtering methods, that firstly apply a basis to the functional data and then use clustering techniques to the obtained data (Abraham et al (2003), Rossi et al (2004), Peng, Müller, et al (2008), Kayano et al (2010)); adaptive methods, where dimensionality reduction and clustering are performed at the same time (James and Sugar (2003), Jacques and Preda (2013), Giacofci et al (2013), Traore et al (2019)); and distancebased methods, which apply a clustering technique based on distances with a specific distance for functional data (Tarpey and Kinateder (2003), Ieva et al (2011), Martino et al (2019). Recent works which perform different strategies for clustering functional data are Zambom et al (2019) that propose a new method applying k-means, assigning each element to a cluster or another based on a combination of an hypothesis test of parallelism and a test for equality of means, and Schmutz et al (2020) that presents a new strategy for clustering functional data based on applying model based techniques when a principal component analysis is previously performed.…”