Automatic classification of eddy current signals based on kernel methods

Abstract

Eddy current nondestructive evaluation techniques are widely used in structural integrity and health monitoring. A novel algorithm based on kernel methods was proposed for characterising eddy current (EC) signals. In scanning inspection, the EC signals responding to the impedance change were pre-processed for noise elimination using the wavelet packet analysis method. Then, the Morlet wavelet was employed to perform the decomposition of one-dimension differential signals onto the coefficients of the wavelet transforms at different scales as input to kernel principal component analysis (KPCA) for feature extraction. After feature extraction, support vector machine (SVM) was carried out to classify EC signals. It is shown by extensive experiments that KPCA is better than the principal component analysis for feature extraction. The kernel methods using SVM by KPCA feature extraction can perform better than the other classification methods.

Keywords:

• eddy current nondestructive evaluation
• classification
• kernel methods
• kernel principal component analysis
• support vector machine

Acknowledgements

The authors would like to thank Prof. G.Y. Tian of the University of Newcastle upon Tyne for his great help and valuable advice. The support of the National Natural Science Foundation of China (NSFC) by Grant 50505045 is gratefully appreciated.