Nuclear power plant fault diagnosis using neural networks with error estimation by series association

IEEE Transactions on Nuclear Science

Volumn 43, Issue 4 PART 2, 1996, Pages 2373-2388

  Kim, Keehoon ^a   Bartlett, Eric B ^b  

^a Korea Electric Power Research Institute (KEPRI)   (South Korea)

^b Iowa State University of Science and Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; ERROR ANALYSIS; ESTIMATION; FAILURE ANALYSIS; NEURAL NETWORKS; TRANSIENTS;

ANTICIPATED TRANSIENT WITHOUT SCRAM; ERROR ESTIMATION BY SERIES ASSOCIATION; FAULT DIAGNOSTIC ADVISOR; SECONDARY ERROR PREDICTION NETWORK;

NUCLEAR POWER PLANTS;

EID: 0030216881     PISSN: 00189499     EISSN: None     Source Type: Journal    
DOI: 10.1109/23.531786     Document Type: Article

References (34)

1. B. R. Upadhyaya and E. EryurekApplication of neural networks for sensor validation and plant monitoringNuci TechnoL, vol. 97, pp. 170-176, 1992.
2. A. Ikonomopoulos; L. H. Tsoukalas and R. E. UhrigMonitoring nuclear reactor systems using neural networks and fuzzy logicProc. Top. Meet.. Advances Reactor Physics, Charleston, SC, Mar. 1992, pp. 2-14.
3. Z. Quo and R. E. UhrigUse of artificial neural networks to analyze nuclear power plant performanceNucl. TechnoL. vol. 99, pp. 36-42, 1992
4. I. E. Alguindigue, R. E. Uhrig, M. Cai, and A. TrendyDiscrimination of ex-core neutron noise signature using artificial neural networksPntc. American Povver Conf., Chicago, IL, Apr. 12-14, 1993.
5. M. Roh, S. Cheon, and S. ChangThermal power prediction of nuclear power plant using neural network and parity space modelIEEE Trans. Nucl. Sei., vol. 38, no. 2, pp. 866-872, Apr. 1991.
6. H. Kirn and S. LeeNeural network model for estimating departure from nuclear boiling performance of a pressurized water reactor coreNucl. TechnoL vol. 101, pp. 111-122, 1993.
7. II. G. Kirn. S. H. Chang and B. H. LeePressurized water reactor core parameter prediction using an artificial neural networkNucl. Sei. Eng., vol. 113, pp. 70-76, 1993.
8. 18| W. C. Jouse and J. G. WilliamsNeural control of temperature and pressure during PWR start-upTrans. Amer. Nucl. Soc., vol. 61, pp. 219-220, 1990.
9. E. B. Bartlett and R. E. UhrigNuclear power plant status diagnostics using an artificial neural network. Nucl. TechnoL, vol. 97, pp. 272-281, 1992.
10. Y. Ohga and H. SekiAbnormal event identification in nuclear power plants using a neural network and knowledge processingNucl. Techno!., vol. 101, pp. 159-167, 1993.
11. S. W. Cheon arid S. H. ChangApplication of neural networks to a connectionist expert system for transient identification in nuclear power plantsNucl. TechnoL, vol. 102, pp. 177-191, 1993.
12. A. G. Parles, J. Muthusami, and A. F. AtiyaIncipient fault detection and identification in process systems using accelerated neural network learningNucL Technol., vol. 105, pp. 145-161, 1994.
13. K. Kim, T. L. Aljundi, and E. B. BartlettNuclear power plant fault diagnostic using artificial neural networksProc. Intelligent Engineering System!, Through Artificial Neural Networks, vol. 2. New York: ASME Press, 1992, pp. 751-756.
14. _____Confirmation of artificial neural networks: nuclear power plant fault diagnosticsTrans. Amer. Nucl. Soc., vol. 66, pp. 112-114. 1992.
15. K. Kim and E. B. BartlettError prediction for a nuclear power plant fault-diagnostic advisor using neural networksNucL Technol., vol. 108, pp. 283-297, 1994
16. M. StoneCross-validatory choice and assessment of statistical predictions J. Royal Statistical Society Series, vol. B36, pp. 111-147, 1974.
17. M. StoneAsymptotics for and against cross-validation,'' Biometrica, vol. 64, pp. 29-35, 1977.
18. [181 S. GeisscrThe predictive sample reuse method with applicationsJ. Amer. Statistical Assoc., vol. 70, pp. 320-328, 1975.
19. K. LiFrom Stein's unbiased risk estimates to the method of generalized cross-validationThe Annals of Statistics, vol. 13, pp. 1352-1377. 1985.
20. D. H. WolpertStacked generalizationIEEE Trans. Neural Networks, vol. 5, 241-259, 1992.
21. K. KimReliability assessment of nuclear power plant fault-diagnostic systems using artificial neural networks Ph.D. dissertation, Iowa State Univ., Ames. 1A, 1994.
22. H. S. Wilf, Algorithms and Complexity. Englewood Cliffs, NJ: Prentice-Hall, 1986.
23. J. S. Judd, Neural Network Design and the Complexity of Learning. Cambridge, MA: MIT Press, 1990.
24. J. Gould. Malfunction cause and effects report, task no. 06000004 Duane Arnold Energy Center, Iowa Electric Light and Power Co., Palo, IA, 1991.
25. Accident analysis in Updated Final Safety Analysis Report vol. XI, Duane Arnold Energy Center, Iowa Electric Light and Power Co., Palo, Iowa, 1984, ch. 15.
26. G. CyberJtoApproximation by superposition of a sigmoidal functionMathematics of Control, Signals, and Systems, vol. 2, pp. 303-31, 1989.
27. D. II. WolpertA mathematical theory of generalization: Part I and part IIComplex Systems, vol. 4, pp. 151-249, 1990.
28. V. KurkovaKolmogorov's theorem and multilayer neural networksIEEE Trans. Neural Networks, vol. 5. pp. 501-506, 1992.
29. A. Lapades and R. FarberNonlinear signal processing using neural networks: Prediction and system modeling Los Alamos National Laboratory Tech. Rep. LA-UR-87-2662, 1987.
30. K. S. Narendra and K. ParthasarathyIdentification and control of dynamic systems using neural networksIEEE Trans. Neural Networks, vol. 1, no. 1, pp. 4-26, 1990.
31. E. K. Blum and L. K. LiApproximation theory and feedforward networksIEEE Trans. Neural Networks, vol. 4, pp. 511-515, 1991.
32. D. E. Rumelhart et al., Parallel Distributed Processing: Exploration in the Microstructure of Cognition, vols. 1 and 2. Cambridge, MA: MIT Press, 1986.
33. R. P. LippmannAn introduction to computing with neural netsIEEE Acoustics Speech Signal Processing Mag., vol. 4, pp. 4-22, 1987.
34. [34J R. Hecht-Nielsen, Neurocomputing. Reading, MA: Addison-Wesley, 1990.