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Neural network for inverse mapping in eddy current testing

Domenii publicaţii > Stiinte ingineresti + Tipuri publicaţii > Articol în volumul unei conferinţe

Autori: Preda, G., Popa, R.C., Demachi, K., Miya, K.,

Editorial: IJCNN'99, International Joint Conference on Neural Networks, 6, p.4033-4036, 1999.


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 non-connected crack shapes even in the presence of high noise levels.

Cuvinte cheie: Neural Networks, ECT, NDT