A modified Auto Associative Kernel Regression method for robust signal reconstruction in nuclear power plant components

Piero Baraldi (1) , Francesco Di Maio (1) , Pietro Turati (1) , Enrico Zio (2)

1 Dipartimento di Energia [Milano]
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

The application of the Auto Associative Kernel Regression (AAKR) method to the reconstruction of correlat-ed plant signals is not satisfactory from the point of view of the robustness, i.e. the capability of reconstruct-ing abnormal signals to the values expected in normal conditions. To overtake this limitation, we propose to modify the traditional AAKR method by defining a novel measure of the similarity between the current measurement and the historical patterns. An application of the proposed modified AAKR method to the con-dition monitoring of a pressurizer of a Pressurized Water Reactor (PWR) Nuclear Power Plant (NPP) shows benefits with respect to the traditional AAKR method, in terms of earlier detection of abnormal conditions and correct identification of the signals responsible for triggering the detection.

Mots clés: Signal Reconstruction; Fault Detection

Domaines: Traitement du signal et de l'image [eess.SP]; Génie civil nucléaire; Risques

Piero Baraldi, Francesco Di Maio, Pietro Turati, Enrico Zio. A modified Auto Associative Kernel Regression method for robust signal reconstruction in nuclear power plant components. European Safety and Reliability Conference ESREL, Sep 2014, Wroclaw, Poland. ⟨hal-01262146⟩