Statistical Dynamic Classification to Detect Changes in Temperature Time Series
This article deals with the problem of change detection in the output temperature time series of the Phenix nuclear reactor core assemblies. These time series are provided by the Atomic Energy and Alternative Energies Commission (CEA). A hypothetical and theoretical blockage of an assembly cooling system could lead to a temperature rise of its nearest neighbours. To detect such a rise, first a signal preprocessing has been realized in several steps: simulation of a blockage, filtering, interpolation and re-sampling. Then, several statistical estimators have been calculated on sliding windows. The feature space has been determined based on the most discriminant parameters, including a derived third order moment. Finally, a set of classification rules has been defined to detect an assembly blockage. Thus, a statistical dynamic classification is realized online to obtain at most two classes. Results have been validated on several assemblies with different realistic perturbations.
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