Analysis of defectoscopy data to be used by neural classifier

At present a very perspective solution of indications classification in defectoscopy is neural network application. One of the fields is classification of indications into classes that are. characterized by the signal shape, or by the signatures relating to the signal shape. Nondestructive defectoscopy of steam generator tubes of nuclear power plants by multifrequency eddy current method is the field in which the use of classifiers based on neural network architecture is very perspective. The contribution concentrates on the choice of a suitable representation of indications for neural classifier represented by probabilistic neural network. Selected representations are compared using real records of steam generator tubes and also using artificial defects and imitations of construction elements.