Abstract
Adaptive Resonance Theory (ART) represents a family of neural networks each having its own unique characteristics. This paper demonstrates the capability of ART2-A network in performing the challenging task of pattern recognition of complex noisy signals from nuclear plant components. In addition, its capability in pattern recognition of acoustic signature is briefly addressed. The results show that an ART2-A network can be successfully used both as an unsupervised pattern classifier and as a pseudo-supervised network for fault identification in a nuclear reactor system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
R. E. Uhrig, “Artificial Neural Network and Potential Applications to Nuclear Power Plants,” Post-Conference Seminar, Structural Mechanics in Reactor Technology, University of Stuttgart, Germany, Agust 1993.
Z. Guo and R. E. Uhrig, “Use of Artificial Neural Networks to Analyze Nuclear Power Plant Performance,” Nuclear Technology, Vol. 99, pp 36–42, 1992.
S. Keyvan, A. Durg, L. C. Rabelo, “Application of Artificial Neural Networks for Development of Diagnostic Monitoring System in Nuclear Plants”, Proceedings of ANS Topical Meeting on Nuclear Plant Instrumentation, Control and Man-Machine Interface Technologies, Vol. 1, pp. 515–522, April, 1993.
S. Keyvan and R. Pickard, “Feature Extraction of Metal Impact Acoustic Signals For Pattern Classification by Neural Networks,” Journal of Acoustic Emission, Vol. 15, No. 1-4, pp 79–87, 1998.
G. A. Carpenter, and S. Grossberg, “ART 2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns,” Applied Optics, vol. 26, pp. 4919–4930, 1987.
G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, “Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Anlog Multidimensional MAPs”, IEEE Transactions on Neural Networks, Vol 3, No. 5, Sept. 1992.
G. A. Carpenter, S. Grossberg, and D. B. Rosen “ART2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition,” Neural Networks 4, 1991.
S. Keyvan, A. Durg, and J. Nagaraj, “Application of Artificial Neural Network for Development of a Signal Monitoring System,” Expert Systems, Volume 14, No 2, pp 69–79, 1997.
S. Keyvan and A. Durg, “Pump Shaft Condition Monitoring via Artificial Neural Networks”, Proceedings of the 5th Predictive Maintenance Conference, sponsored by EPRI, Knoxville, TN, September 1992.
S. Keyvan, L. C. Rabelo. “Nuclear Reactor Pump shaft diagnostics via Noise Analysis/Artificial Neural Networks” Intelligent Engineering Systems Through Artificial Neural Networks, C. H. Dagli, S. R. T. Kumara, Y. C. Shin editors, ASME Press, New York, pp. 651–656, 1991.
S. Keyvan, W. Khan, “ART2-A Vigilance Parameter Optimization using Genetic Algorithm” Mathematical Modeling and Scientific Computing, Proceedings of the 10th International Conference, Volume 6, 1996.
S. Keyvan and J. Nagaraj, “Pattern Recognition of Acoustic Signatures using ART2-A Neural Network,” Journal of Acoustic Emission, Vol. 14, No. 2, pp 97–102, 1997.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Keyvan, S., Rabelo, L.C. (1999). Application of ART2-A as a pseudo-supervised paradigm to nuclear reactor diagnostics. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098233
Download citation
DOI: https://doi.org/10.1007/BFb0098233
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66069-9
Online ISBN: 978-3-540-48771-5
eBook Packages: Springer Book Archive