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Nuclear reactor monitoring with the combination of neural network and expert system

Author

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  • Nabeshima, K
  • Suzudo, T
  • Ohno, T
  • Kudo, K

Abstract

This study presents a hybrid monitoring system for nuclear reactor utilizing neural networks and a rule-based real-time expert system. The whole monitoring system including a data acquisition system and the advisory displays has been tested by an online simulator of pressurized water reactor. From the testing results, it was shown that the neural network in the monitoring system successfully modeled the plant dynamics and detected the symptoms of anomalies earlier than the conventional alarm system. The real-time expert system also worked satisfactorily in diagnosing and displaying the system status by using the outputs of neural networks and a priori knowledge base.

Suggested Citation

  • Nabeshima, K & Suzudo, T & Ohno, T & Kudo, K, 2002. "Nuclear reactor monitoring with the combination of neural network and expert system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 233-244.
    • Handle: RePEc:eee:matcom:v:60:y:2002:i:3:p:233-244
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    Cited by:

    1. Tolo, Silvia & Tian, Xiange & Bausch, Nils & Becerra, Victor & Santhosh, T.V. & Vinod, G. & Patelli, Edoardo, 2019. "Robust on-line diagnosis tool for the early accident detection in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 110-119.

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