Pattern recognition approaches for the detection and characterization of discontinuities by eddy current testing

Eddy current signals (ECS) generated under varied experimental conditions f rom different types of discontinuities like partial/through thickness holes and notches of various dimensions, fatigue cracks, stress corrosion cracks , etc, in AISI type 316 stainless steel sheets/plates have been analyzed us ing pattern recognition (PR) approaches to understand their quality of perf ormance for detection and characterization of several aspects of the discon tinuities. The PR analyses have been carried out using linear discriminant (LD), minimum distance (MD), empirical Bayesian (EB) and K- nearest neighbo r (KNN) statistical classifiers, and multilayered perceptron (MLP) and Koho nen's artificial neural network (KANN). The MLP approach has been extended to eddy current images also to achieve deblurring. The practical feasibilit y and application potential of ANNs is demonstrated through a case study on nuclear fuel cladding tubes where both the online and the offline approach es have been implemented.