Abstract
Eddy current nondestructive evaluation (ECNDE) techniques are widely used in structural integrity and health monitoring. A novel algorithm was proposed for characterizing eddy current (EC) signals. In scanning inspection, the EC signals responding to impedance change were pre-processed for noise elimination and feature extraction. After feature extraction, Bayesian networks (BNs) were carried out to classify EC signals. It is shown by extensive experiments that kernel principal component analysis (KPCA) is better than principal component analysis (PCA) for feature extraction. The methods using BNs by KPCA feature extraction can perform better than the other classification methods.
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