Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
C. Rajagopalan, Baldev Raj, P. Kalyanasundaram, “The Role of Articial Intelligence in Nondestructive Testing and Evaluation”, INSIGHT, Vol 38, No 2, February 1996.
C. Charlton, “Investigation into the Suitability of a Neural Network Classifier for use in an Automated Tube Inspection System”, British Journal of NDT, vol..35, No 8, August 1993.
Fu. LiMin, “Neural Networks in Computer Inteligence”, McGraw-Hill Companies, March 1994.
P.D. Wasserman, “Advanced Methods in Neural Computing”, New York: Van Nostrand Reinhold, 1993, pp. 35-55, 155–61
Ch.T. Yahn, R.Z. Roskies, “Fourier Descriptors for Plane Closed Curves”, IEEE Transactions on computers, March 1972
R. Palanisamy, W. Loyd, “Finite Element Simulation of Support Plate and Tube Defect Eddy Curre nt Signals in Steam Generator NDT”, Materials Evaluation, Vol 39, June 1981.
J. Grman, “Neural Network Application in the Defectoscopy”, IMEKO 2000 — XVI IMEKO World Congress, Wienna, Austria, 25.–28.September 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Wien
About this paper
Cite this paper
Grman, J., Ravas, R., Syrova, L. (2001). Analysis of Defectoscopy Data to Be Used by Neural Classifier. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_47
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6230-9_47
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
Online ISBN: 978-3-7091-6230-9
eBook Packages: Springer Book Archive