Affiliations: [a] NeuroLab, Faculty of Engineering, University of Reggio
Calabria, Via Graziella - Feo di Vito, 89100 Reggio Calabria, Italy. Tel.: +39
0965 875 224; Fax: +39 0965 875 220; E-mail: simoneg@ing.unirc.it,
morabito@unirc.it; Web: http://neurolab.ing.unirc.it | [b] MARG, Iowa State University, 391 Durham Center, IA
50011-2252, Ames, USA. E-mail: rpolikar@iastate.edu; rpradeep@iastate.edu;
lalita@iastate.edu; udpa@iastate.edu; Web: http://marg.ee.iastate.edu
Abstract: In this paper, we present two feature extraction techniques for the
classification of ultrasonic NDE signals acquired from weld inspection regions
of boiling water reactor piping of nuclear power plants. The classification
system consists of a pre-processing block that extracts features from the
incoming patterns, and of an artificial neural network that assigns the
computed features to a particular class of defect present on the inspected
pipe. The two techniques are respectively based on the discrete Gabor transform
(DGT) and on the discrete wavelet transform (DWT); a third feature extraction
technique, based on the clustering of the wavelet coefficients, is also
presented. The results carried out by artificial neural networks trained and
tested using the described feature extraction techniques, demonstrate the
usefulness of the clustered DWT method with respect to the well known
techniques of DGT and DWT.
