Inversion of experimental eddy current testing signals obtained from steam generator tubes - Approach and automated system

Enhanced interpretation tools for eddy current testing (ECT) signals have been developed with a novel combination of neural networks and finite element modelling for quantitative flaw characterization in steam generator tubes. A database was constructed using synthetic ECT signals generated by the finite element models and principal component analysis (PCA) was adopted in order to optimize the feature set of ECT signals. Using the features with PCA, Excellent performances for the experimental ECT signals demonstrate the high potential of the developed inversion tools for reliable interpretation of eddy current signals. To explore the possibility of applying the developed approach in practical inspection, we developed an automated system (laboratory prototype) that can acquire experimental ECT signals from SG tubes and carry out the quantitative flaw characterization in a real time fashion by applying the approach developed in the present work.