A data-driven approach for predicting failure scenarios in nuclear systems

Abstract : A data-driven approach is presented for the on-line identification of the system Failure Mode (FM) and the prediction of the available Recovery Time (RT) during a failure scenario, i.e., the time remaining until the system can no longer perform its function in an irreversible manner. The FM identification and RT prediction modules are linked in a general framework that recognizes the patterns of dynamic evolution of the process variables in the different system failure modes. When a new failure scenario develops, its evolution pattern is compared by fuzzy similarity analysis to a library of reference multidimensional trajectory patterns of process variables evolution; the failure mode of the developing scenario is identified by combining the modes of failure of the reference patterns, weighed by their similarity to the developing pattern; the similarity weights are then fed to the RT prediction module that estimates the time remaining before the developing trajectory pattern hits a failure threshold. The approach is illustrated on failure scenarios of the Lead-Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The accident scenarios are classified in three different system failure modes, depending on the value reached by the diathermic oil secondary coolant temperature with respect to maximum and minimum safety threshold values set to avoid primary coolant thermal shocks and degradation of the oil physical and chemical properties.
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Enrico Zio, Francesco Di Maio, Marco Stasi. A data-driven approach for predicting failure scenarios in nuclear systems. Annals of Nuclear Energy, Elsevier Masson, 2010, 37 (4), pp.482-491. ⟨10.1016/j.anucene.2010.01.017⟩. ⟨hal-00610503⟩

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