Abstract
In this chapter, a transient identification based on a hidden Markov model (HMM) has been suggested and evaluated experimentally for the classification of transients in nuclear power plants. The transient can be identified by its unique time dependent patterns related to the principal variables. The HMM, a double stochastic process, can be applied to transient identification which is a spatial and temporal classification problem under a statistical pattern recognition framework. The HMM is created for each transient from a set of training data by the maximum-likelihood estimation method. The transient identification is determined by calculating which model has the highest probability for the given test data. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. Several experimental tests have been performed including superimposing random noise, adding systematic error, and untrained transients. The proposed real-time transient identification system has many advantages, however, there are still a lot of problems that should be solved to apply to a real dynamic process. Further efforts are being made to improve the system performance and robustness to demonstrate reliability and accuracy to the required level.
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
I. S. Kim, “Computerized Systems for On-line Management of Failures: A State-Of-The-Art Discussion of Alarm and Diagnostic Systems Applied in the Nuclear Industry,” Reliability Engineering and System Safety, Vol. 44, pp. 279–295, 1994.
A. Ikonomopoulos, R. E. Uhrig, and L. Tsoukalas, “A Hybrid Neural Network-Fuzzy Logic Approach to Nuclear Power Plant Transient Identification,” AI91 Frontiers in Innovative Computing for the Nuclear Industry, pp.217–226, Jackson, Wyoming, Sep. 15–18, 1991.
Y. Bartal, J. Lin, and R. Uhrig, “Transients Identification in Nuclear Power Plants Using Probabilistic Neural Networks and the Problem of Knowledge Extrapolation,” 9th Power Plant Dynamics, Control & Testing Symposium, pp.49.01–49.08, Knoxville, TN, USA, May 24–26, 1995.
Z. Guo, R. E. Uhrig, “Accident Scenario Diagnostics with Neural Networks,” 8th Power Plant Dynamics, Control & Testing Symposium, pp.53.01–53. 11, Knoxville, TN, USA, May 1992.
S. W. Cheon, “Application of Neural Networks to a Connectionist Expert System for Transient Identification in Nuclear Power Plants,” Nuclear Technology, Vol. 102, pp. 177–191, May 1993.
T. Iijima, Application Study of Fuzzy Logic method for Plant-State Identification, HWR-432, OECD Halden Reactor Project, Dec. 1995.
J. Lin, Y. Bartal, and R. Uhrig, “Using Similarity Based Formulas and Genetic Algorithms to Predict the Severity of Nuclear Power Plant Transients,” 9th Power Plant Dynamics, Control & Testing Symposium, pp.53.01–53.09, Knoxville, TN, USA, May 24–26, 1995.
E. Jeong, K. Furuta, and S. Kondo, “Identification of Transient in Nuclear Power Plant Using Adaptive Template Matching with Neural Network,” Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, The Penn State Univ. USA, pp.243–250, May 6–9, 1996.
J. W. Hines, D. W. Miller, and B. K. Hajek, “A Hybrid Approach for Detecting and Isolating Faults in Nuclear Power Plant Interacting Systems,” Nuclear Technology, Vol. 115, pp. 342–358, Sep. 1996.
P. Smyth, “Tutorial Material; Machine Learning: Theory and Application,” The 3 rd World Congress on Expert Systems, Seoul, Korea, Feb. 5–9, 1996.
X. D. Huang, Y. Ariki, and M. A. Jack, Hidden Markov Models for Speech Recognition, Edinburgh University Press, Edinburgh, 1990.
Y. Bartal, J. Lin, and R. Uhrig, “Nuclear Power Plant Transient Diagnostics Using Artificial Neural Networks that Allow ”Don’t-Know“ Classifications,” Nuclear Technology, Vol. 110, pp. 436–449, Jun. 1995.
C. Couvreur, Environmental Sound Recognition: A Statistical Approach, Ph. D Thesis, Faculté Polytechnique de Mons, Belgium, June 1997.
L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Application in Speech Recognition,” Proceedings of the IEEE, Vol. 77, No. 2, pp. 257–285, Feb. 1989.
T. Kohonen, “The Self-Organizing Map,” Proceedings of the IEEE, Vol. 78, No. 9, pp. 1464–1480, Sep. 1990.
K. C. Kwon, S. J. Song, W. M. Park, and S. P. Lyu, “The Real-time Functional Test Facility for Advanced Instrumentation and Control in Nuclear Power Plants,” IEEE Transactions on Nuclear Science, Vol. 46, No. 2, pp. 92–99, April 1999.
E. Jeong, K. Furuta, and S. Kondo, “Identification of Transient in Nuclear Power Plant Using Neural Network with Implicit Time Measure,” Proceedings of the Topical Meeting on Computer-Based Human Support Systems, pp.467–474, Philadelphia, PA, USA, June 25–29, 1995.
T. Applebaum and B. Hanson, “Enhancing the Discrimination of Speaker Independent Hidden Markov Models with Corrective Training,” Proc. ICASSP-89, pp.302–305, Glasgow, Scotland, May 1989.
Y. Ohga and H. Seki, “Abnormal Event Identification in Nuclear Power Plants Using a Neural Network and Knowledge Processing,” Nuclear Technology, Vol.101, pp.159167, Feb. 1993.
A. Kundu and G. Chen, “An Integrated Hybrid Neural Network and Hidden Markov Model Classifier for Sonar Signal Classification,” ICASSP, pp. 3587–3590, 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kwon, KC. (2002). Hidden Markov Model Based Transient Identification in NPPs. In: Ruan, D., Fantoni, P.F. (eds) Power Plant Surveillance and Diagnostics. Power Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04945-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-662-04945-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07754-8
Online ISBN: 978-3-662-04945-7
eBook Packages: Springer Book Archive