Neural network for inverse mapping in eddy current testing

Abstract

A neural network mapping approach has been proposed for the inversion problem in eddy-current testing (ECT). The use of a principal component analysis (PCA) data transformation step, a data fragmentation technique, jittering, and of a data fusion approach proved to be instrumental auxiliary tools that support the basic training algorithm in coping with the strong ill-posedness of the inversion problem. The present paper reports on the further improvements brought by a new, randomly generated database used for the training set, proposed for the reconstruction of crack shape and conductivity distribution. Good results were obtained for four levels of conductivity and nonconnected crack shapes even in the presence of high noise levels.