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

[+] Author Affiliations
M. Yang, Z. J. Zhang, M. J. Peng, S. Y. Yan, H. Wang

Harbin Engineering University, Harbin, China

J. Ouyang

Japan Kyoto University, Kyoto, Japan

Paper No. ICONE14-89554, pp. 205-209; 5 pages
  • 14th International Conference on Nuclear Engineering
  • Volume 1: Plant Operations, Maintenance and Life Cycle; Component Reliability and Materials Issues; Codes, Standards, Licensing and Regulatory Issues; Fuel Cycle and High Level Waste Management
  • Miami, Florida, USA, July 17–20, 2006
  • Conference Sponsors: Nuclear Engineering Division
  • ISBN: 0-7918-4242-8 | eISBN: 0-7918-3783-1
  • Copyright © 2006 by ASME


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.

Copyright © 2006 by ASME



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