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Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor

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  • Martinez-Martinez, Sinuhe
  • Messai, Nadhir
  • Jeannot, Jean-Philippe
  • Nuzillard, Danielle

Abstract

The total instantaneous blockage (TIB) of an assembly in the core of a sodium-cooled fast reactor (SFR) is investigated. Such incident could appear as an abnormal rise in temperature on the assemblies neighbouring the blockage. Its detection relies on a dataset of temperature measurements of the assemblies making up the core of the French Phenix Nuclear Reactor. The data are provided by the French Commission of Atomic and Alternatives Energies (CEA). Here, two strategies are proposed depending on whether the sensor measurement of the suspected assembly is reliable or not. The proposed methodology implements a time-lagged feed-forward neural (TLFFN) Network in order to predict the one-step-ahead temperature of a given assembly. The incident is declared if the difference between the predicted process and the actual one exceeds a threshold. In these simulated conditions, the method is efficient to detect small gradients as expected in reality.

Suggested Citation

  • Martinez-Martinez, Sinuhe & Messai, Nadhir & Jeannot, Jean-Philippe & Nuzillard, Danielle, 2015. "Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 50-57.
    • Handle: RePEc:eee:reensy:v:137:y:2015:i:c:p:50-57
    DOI: 10.1016/j.ress.2014.12.003
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    References listed on IDEAS

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    1. Santosh, T.V. & Vinod, Gopika & Saraf, R.K. & Ghosh, A.K. & Kushwaha, H.S., 2007. "Application of artificial neural networks to nuclear power plant transient diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1468-1472.
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    Cited by:

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    2. Kowal, Karol & Torabi, Mina, 2021. "Failure mode and reliability study for Electrical Facility of the High Temperature Engineering Test Reactor," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. Alexandra Akins & Derek Kultgen & Alexander Heifetz, 2023. "Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder," Energies, MDPI, vol. 16(13), pages 1-19, June.

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