Volume 7, Issue 3, June 2014, Pages 481 - 492
Empirical Mode Decomposition and Rough Set Attribute Reduction for Ultrasonic Flaw Signal Classification
Peng Yang, Qintian Yang
Received 30 May 2012, Accepted 29 December 2013, Available Online 1 June 2014.
- https://doi.org/10.1080/18756891.2014.889877How to use a DOI?
- empirical mode decomposition, rough set attribute reduction, feature extraction and selection, ultrasonic flaw signal classification
- 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.
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 -