Eddy current flaw characterization using neural network

Determination of location, shape and size of a flaw from its eddy current testing signal is one of the fundamental issues in eddy current nondestructive evaluation of steam generator tubes. Here, we propose an approach to this problem; an inversion of eddy current flaw signal using neural networks trained with finite element model-based synthetic signatures. Total 216 eddy current signals from four different types of 2-dimensional axisymmetric flaws in tubes are generated by finite element models of which the accuracy are experimentally verified. From each simulated signature, total 24 eddy current features are extracted and among them 13 features are finally selected for the flaw characterization. Based on these features, probabilistic neural networks discriminate flaws into four different types according to the location and the shape, and successively back propagation neural networks determine the size parameters of the discriminated flaw.