International Journal of Computational Intelligence Systems

Volume 7, Issue 3, June 2014, Pages 481 - 492

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

Authors
Peng Yang, Qintian Yang
Corresponding Author
Peng Yang
Received 30 May 2012, Accepted 29 December 2013, Available Online 1 June 2014.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
7 - 3
Pages
481 - 492
Publication Date
2014/06
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2014.889877How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Peng Yang
AU  - Qintian Yang
PY  - 2014
DA  - 2014/06
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  - https://doi.org/10.1080/18756891.2014.889877
ID  - Yang2014
ER  -