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

被引:92
作者
Morizet, N. [1 ]
Godin, N. [1 ]
Tang, J. [1 ]
Maillet, E. [1 ]
Fregonese, M. [1 ]
Normand, B. [1 ]
机构
[1] Inst Natl Sci Appl, MATEIS Lab, UMR CNRS 5510, F-69621 Villeurbanne, France
关键词
Acoustic emission; Corrosion monitoring; Wavelets; Random Forests; Supervised classification; Machine learning; MONITORING PITTING CORROSION; STAINLESS-STEEL;
D O I
10.1016/j.ymssp.2015.09.025
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
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. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1026 / 1037
页数:12
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