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Title: A Study on a Hybrid Approach for Diagnosing Faults in Nuclear Power Plant

Abstract

Proper and rapid identification of malfunctions is of premier importance for the safe operation of Nuclear Power Plants (NPP). Many monitoring or/and diagnosis methodologies based on artificial and computational intelligence have been proposed to aid operator to understand system problems, perform trouble-shooting action and reduce human error under serious pressure. However, because no single method is adequate to handle all requirements for diagnostic system, hybrid approaches where different methods work in conjunction to solve parts of the problem interest researchers greatly. In this study, Multilevel Flow Models (MFM) and Artificial Neural Network (ANN) are proposed and employed to develop a fault diagnosis system with the intention of improving the success rate of recognition on the one hand, and improving the understandability of diagnostic process and results on the other hand. Several simulation cases were conducted for evaluating the performance of the proposed diagnosis system. The simulation results validated the effectiveness of the proposed hybrid approach. (authors)

Authors:
; ; ; ;  [1];  [2]
  1. Harbin Engineering University, Harbin (China)
  2. Graduate School of Energy Science, Kyoto University, Sakyo-ku, Kyoto 606-8501 (Japan)
Publication Date:
Research Org.:
The ASME Foundation, Inc., Three Park Avenue, New York, NY 10016-5990 (United States)
OSTI Identifier:
20995615
Resource Type:
Conference
Resource Relation:
Conference: 14. international conference on nuclear engineering (ICONE 14), Miami, FL (United States), 17-20 Jul 2006; Other Information: Country of input: France
Country of Publication:
United States
Language:
English
Subject:
22 GENERAL STUDIES OF NUCLEAR REACTORS; ERRORS; FAULT TREE ANALYSIS; FLOW MODELS; HUMAN POPULATIONS; MONITORING; NEURAL NETWORKS; NUCLEAR POWER PLANTS; REACTOR OPERATION; SIMULATION

Citation Formats

Yang, M, Zhang, Z J, Peng, M J, Yan, S Y, Wang, H, and Ouyang, J. A Study on a Hybrid Approach for Diagnosing Faults in Nuclear Power Plant. United States: N. p., 2006. Web.
Yang, M, Zhang, Z J, Peng, M J, Yan, S Y, Wang, H, & Ouyang, J. A Study on a Hybrid Approach for Diagnosing Faults in Nuclear Power Plant. United States.
Yang, M, Zhang, Z J, Peng, M J, Yan, S Y, Wang, H, and Ouyang, J. 2006. "A Study on a Hybrid Approach for Diagnosing Faults in Nuclear Power Plant". United States.
@article{osti_20995615,
title = {A Study on a Hybrid Approach for Diagnosing Faults in Nuclear Power Plant},
author = {Yang, M and Zhang, Z J and Peng, M J and Yan, S Y and Wang, H and Ouyang, J},
abstractNote = {Proper and rapid identification of malfunctions is of premier importance for the safe operation of Nuclear Power Plants (NPP). Many monitoring or/and diagnosis methodologies based on artificial and computational intelligence have been proposed to aid operator to understand system problems, perform trouble-shooting action and reduce human error under serious pressure. However, because no single method is adequate to handle all requirements for diagnostic system, hybrid approaches where different methods work in conjunction to solve parts of the problem interest researchers greatly. In this study, Multilevel Flow Models (MFM) and Artificial Neural Network (ANN) are proposed and employed to develop a fault diagnosis system with the intention of improving the success rate of recognition on the one hand, and improving the understandability of diagnostic process and results on the other hand. Several simulation cases were conducted for evaluating the performance of the proposed diagnosis system. The simulation results validated the effectiveness of the proposed hybrid approach. (authors)},
doi = {},
url = {https://www.osti.gov/biblio/20995615}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2006},
month = {Sat Jul 01 00:00:00 EDT 2006}
}

Conference:
Other availability
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