Acoustic monitoring of sodium boiling in ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Acoustic monitoring of sodium boiling in a Liquid Metal Fast Breeder Reactor from autoregressive models
Auteur(s) :
Cherif Geraldo, Issa [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Bose, Tanmoy [Auteur]
Indian Institute of Technology Kharagpur [IIT Kharagpur]
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, Amiya Rajan [Auteur]
Indian Institute of Technology Kharagpur [IIT Kharagpur]
Paumel, Kévin [Auteur]
CEA Cadarache
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Bose, Tanmoy [Auteur]
Indian Institute of Technology Kharagpur [IIT Kharagpur]
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, Amiya Rajan [Auteur]
Indian Institute of Technology Kharagpur [IIT Kharagpur]
Paumel, Kévin [Auteur]
CEA Cadarache
Titre de la revue :
Nuclear Engineering and Design
Pagination :
573-585
Éditeur :
Elsevier
Date de publication :
2014-10-15
ISSN :
0029-5493
Mot(s)-clé(s) en anglais :
Fault detection
Sound analysis
Nuclear plant
Autoregressive models
Classification methods
Sound analysis
Nuclear plant
Autoregressive models
Classification methods
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Acoustique [physics.class-ph]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Machine Learning [stat.ML]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
This paper deals with acoustic monitoring of sodium boiling in a Liquid Metal Fast Breeder Reactor (LMFBR) based on Auto Regressive (AR) models which have low computational complexities. Some authors have used AR models ...
Lire la suite >This paper deals with acoustic monitoring of sodium boiling in a Liquid Metal Fast Breeder Reactor (LMFBR) based on Auto Regressive (AR) models which have low computational complexities. Some authors have used AR models for sodium boiling or sodium-water reaction detection. These works are based on the characterization of the difference between fault free condition and current functioning of the system. However, even in absence of faults, it is possible to observe a change in the AR models due to the change of operating mode of the LMFBR. This sets up the delicate problem of how to distinguish a change in operating mode in absence of faults and a change due to presence of faults. In this paper we propose a new approach for boiling detection based on the estimation of AR models on sliding windows. Afterwards, classification of the models into boiling or non-boiling models is made by comparing their coefficients by two statistical methods, multiple linear regression (LR) and Support Vectors Machines (SVM). The proposed approach takes into account operating mode informations in order to avoid false alarms. Experimental data include non-boiling background noise data collected from Phenix power plant (France) and provided by the CEA (Commissariat à l'Energie Atomique et aux énergies alternatives, France) and boiling condition data generated in laboratory. High boiling detection rates as well as low false alarms rates obtained on these experimental data show that the proposed method is efficient for boiling detection. Most importantly, it shows that the boiling phenomenon introduces a disturbance into the AR models that can be clearly detected.Lire moins >
Lire la suite >This paper deals with acoustic monitoring of sodium boiling in a Liquid Metal Fast Breeder Reactor (LMFBR) based on Auto Regressive (AR) models which have low computational complexities. Some authors have used AR models for sodium boiling or sodium-water reaction detection. These works are based on the characterization of the difference between fault free condition and current functioning of the system. However, even in absence of faults, it is possible to observe a change in the AR models due to the change of operating mode of the LMFBR. This sets up the delicate problem of how to distinguish a change in operating mode in absence of faults and a change due to presence of faults. In this paper we propose a new approach for boiling detection based on the estimation of AR models on sliding windows. Afterwards, classification of the models into boiling or non-boiling models is made by comparing their coefficients by two statistical methods, multiple linear regression (LR) and Support Vectors Machines (SVM). The proposed approach takes into account operating mode informations in order to avoid false alarms. Experimental data include non-boiling background noise data collected from Phenix power plant (France) and provided by the CEA (Commissariat à l'Energie Atomique et aux énergies alternatives, France) and boiling condition data generated in laboratory. High boiling detection rates as well as low false alarms rates obtained on these experimental data show that the proposed method is efficient for boiling detection. Most importantly, it shows that the boiling phenomenon introduces a disturbance into the AR models that can be clearly detected.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
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