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Defect characterization with eddy currents testing using nonlinear-regression feature extraction and artificial neural networks

Rosado, L. S. ; Janeiro, F. M. ; Ramos, P. M. ; Piedade, M. S.

IEEE Transactions on Instrumentation and Measurement Vol. 62, Nº 5, pp. 1207 - 1214, May, 2013.

ISSN (print): 0018-9456
ISSN (online): 1557-9662

Scimago Journal Ranking: 0,70 (in 2013)

Digital Object Identifier: 10.1109/TIM.2012.2236729

Feature extraction and defect parameters estimation from eddy current testing data has received special attention in the last years. Principal component analysis, wavelet decomposition and Fourier descriptors are some of the tools used for feature extraction. Particular interest is devoted to using artificial neural networks to perform parameters estimation and profile reconstruction of defects. This work reports the use of non-linear regressions for feature extraction based on the modeling of the measured response by a set of additive Gaussians and artificial neural networks to estimate the width and depth of defects.