Empirical Mode Decomposition and Rough Set Attribute Reduction for Ultrasonic Flaw Signal Classification

DOI

10.1080/18756891.2014.889877How to use a DOI?

Keywords

empirical mode decomposition, rough set attribute reduction, feature extraction and selection, ultrasonic flaw signal classification

Abstract

Feature extraction and selection are the most important techniques for ultrasonic flaw signal classification. In this study, empirical mode decomposition (EMD) is used to obtain the intrinsic mode functions (IMFs) of original signal, and their corresponding traditional time and frequency domain based statistical parameters are extracted as the initial features. After that, spectral clustering method is used for feature value discretization so that rough set attribute reduction (RSAR) can be applied to implement feature selection. The final features are taken as input of artificial neural networks (ANNs) to train the decision classifier for flaw identification. Experimental results show that compared to conventional wavelet transform based schemes and principal components analysis, EMD combined with RSAR can improve the performance of feature extraction and selection. ANN by using such scheme can effectively classify different ultrasonic flaw signals with high accuracy and low training elapsed time.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Peng Yang
AU  - Qintian Yang
PY  - 2014
DA  - 2014/06/01
TI  - Empirical Mode Decomposition and Rough Set Attribute Reduction for Ultrasonic Flaw Signal Classification
JO  - International Journal of Computational Intelligence Systems
SP  - 481
EP  - 492
VL  - 7
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.889877
DO  - 10.1080/18756891.2014.889877
ID  - Yang2014
ER  -