Classification of acoustic emission signals using wavelets and Random Forests: Application to localized corrosion

N. Morizet , Nathalie Godin (1) , J. Tang , E. Maillet , Marion Fregonese (1) , Bernard Normand (1)

1 MATEIS - Matériaux, ingénierie et science [Villeurbanne]

This paper aims to propose a novel approach to classify acoustic emission (AE) signals deriving from corrosion experiments, even if embedded into a noisy environment. To validate this new methodology, synthetic data are first used throughout an in-depth analysis, comparing Random Forests (RF) to the k-Nearest Neighbor (k-NN) algorithm. Moreover, a new evaluation tool called the alter-class matrix (ACM) is introduced to simulate different degrees of uncertainty on labeled data for supervised classification. Then, tests on real cases involving noise and crevice corrosion are conducted, by preprocessing the waveforms including wavelet denoising and extracting a rich set of features as input of the RF algorithm. To this end, a software called RF-CAM has been developed. Results show that this approach is very efficient on ground truth data and is also very promising on real data, especially for its reliability, performance and speed, which are serious criteria for the chemical industry. © 2015 Elsevier Ltd.

Mots clés: Acoustic emissions; Artificial intelligence; Chemical industry; Corrosion; Decision trees; Learning systems; Nearest neighbor search; Supervised learning; Acoustic emission signal; Corrosion monitoring; Ground truth data; K Nearest Neighbor (k NN) algorithm; Localized corrosion; Random forests; Supervised classification; Wavelets; Acoustic emission testing

Domaines: Matériaux

N. Morizet, Nathalie Godin, J. Tang, E. Maillet, Marion Fregonese, et al.. Classification of acoustic emission signals using wavelets and Random Forests: Application to localized corrosion. Mechanical Systems and Signal Processing, 2016, 70-71, pp.1026-1037. ⟨10.1016/j.ymssp.2015.09.025⟩. ⟨hal-01804466⟩