An eddy currents testing system for the identification of a single crack may be puzzled in the presence of multiple defects. In this paper we propose a hybrid neuro-fuzzy scheme to deal with the possible presence of two neighboring cracks. The procedure consists of a fuzzy pre-processor which recognizes the number of cracks in the region of interest, i.e. classifies the data into different classes associated with the number of defects. The second part of the procedure is based on an array of classical back-propagation neural networks, each of them specialized for data inversion in each class.
Multiple Defect Analysis via Neuro-Fuzzy Approaches
FORMISANO, Alessandro;MARTONE, Raffaele;
1998
Abstract
An eddy currents testing system for the identification of a single crack may be puzzled in the presence of multiple defects. In this paper we propose a hybrid neuro-fuzzy scheme to deal with the possible presence of two neighboring cracks. The procedure consists of a fuzzy pre-processor which recognizes the number of cracks in the region of interest, i.e. classifies the data into different classes associated with the number of defects. The second part of the procedure is based on an array of classical back-propagation neural networks, each of them specialized for data inversion in each class.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
