Autoregressive model-based boiling detection in a Liquid Metal Fast Breeder Reactor

Issa Cherif Geraldo (1) , T. Bose (1) , Komi Midzodzi Pekpe (2) , Jean Philippe Cassar (2) , J. Mohanty

1 Université de Lille, Sciences et Technologies
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

This paper presents a new approach for acoustic detection of sodium boiling in a Liquid Metal Fast Breeder Reactor (LMFBR) based on Autoregressive (AR) models. The AR models are estimated on a sliding window and classified into boiling or non-boiling models by comparing the on-line estimated values of their components to the predictions of their components from the environment parameters using linear regression. In order to avoid false alarms the proposed approach takes into account operating mode information. Promising results are obtained on the background noise data collected from the French Phenix nuclear power plant provided by the French Commission of Atomic and Alternative Energies (CEA). © 2015, IF AC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Mots clés: Autoregressive models; Fault detection; Machine learning; Nuclear plants; Regression analysis; Statistical analysis

Domaines: Sciences de l'ingénieur [physics]; Automatique / Robotique; Traitement du signal et de l'image [eess.SP]

Issa Cherif Geraldo, T. Bose, Komi Midzodzi Pekpe, Jean Philippe Cassar, J. Mohanty. Autoregressive model-based boiling detection in a Liquid Metal Fast Breeder Reactor. 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015, IFAC, Sep 2016, Paris, France. ⟨10.1016/j.ifacol.2015.09.586⟩. ⟨hal-01373463⟩