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Yu Wang *
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 3, Pages 1,401-1,420, DOI: https://doi.org/10.21307/ijssis-2017-712
License : (CC BY-NC-ND 4.0)
Received Date : 15-April-2014 / Accepted: 30-July-2014 / Published Online: 01-September-2014
Feature extraction and selection are the most important techniques for ultrasonic flaw signal
classification. In this study, empirical mode decomposition (EMD) is first used to obtain the intrinsic
mode functions (IMFs) of original ultrasonic signals. Such IMFs and traditional time as well as
frequency domain based statistical parameters are extracted as the initial features of flaw signal. 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. Finally, the selected 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. Using such hybrid scheme can effectively classify different ultrasonic flaw
signals with high accuracy and low training elapsed time.
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