Title :

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

Author(s) :

Cherif Geraldo, Issa [Auteur]
Université de Lille, Sciences et Technologies
Bose, T. [Auteur]
Université de Lille, Sciences et Technologies
Pekpe, Midzodzi [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Cassar, Jean Philippe [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Mohanty, J. [Auteur]

Conference title :

9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015

Conference organizers(s) :

IFAC

City :

Paris

Country :

France

Start date of the conference :

2016-09-02

Journal title :

9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015

Publisher :

Elsevier Ltd

Publication date :

2015-09-01

English keyword(s) :

Autoregressive models
Fault detection
Machine learning
Nuclear plants
Regression analysis
Statistical analysis

HAL domain(s) :

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

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL