Fault diagnosis for an MSF desalination plant by using Bayesian networks

Autores
Tarifa, Enrique Eduardo; Álvaro, F. Núñez; Franco, Samuel; Mussati, Sergio Fabian
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) desalination plant by using BNs (Bayesian networks). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; i.e., it finds out which is the fault that is affecting the supervised process. A BN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. This paper determines both the structure and parameters of a BN intended for a diagnostic system. The implemented system is evaluated by using a dynamic simulator, which was developed for a real MSF desalination plant. Besides, the diagnostic system performance is compared with the performances of two other diagnostic systems. The obtained results show some advantages for the BN based diagnostic system.
Fil: Tarifa, Enrique Eduardo. Universidad Nacional de Jujuy; Argentina
Fil: Álvaro, F. Núñez. Universidad Nacional de Jujuy; Argentina
Fil: Franco, Samuel. Universidad Nacional de Jujuy; Argentina
Fil: Mussati, Sergio Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
BAYESIAN NETWORKS
DYNAMIC SIMULATION
FAULT DIAGNOSIS
MSF DESALINATION PLANT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/192855
Acceder
id CONICETDig_f2c54048eb8e3df33998017a47656421
oai_identifier_str oai:ri.conicet.gov.ar:11336/192855
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Fault diagnosis for an MSF desalination plant by using Bayesian networksTarifa, Enrique EduardoÁlvaro, F. NúñezFranco, SamuelMussati, Sergio FabianBAYESIAN NETWORKSDYNAMIC SIMULATIONFAULT DIAGNOSISMSF DESALINATION PLANThttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) desalination plant by using BNs (Bayesian networks). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; i.e., it finds out which is the fault that is affecting the supervised process. A BN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. This paper determines both the structure and parameters of a BN intended for a diagnostic system. The implemented system is evaluated by using a dynamic simulator, which was developed for a real MSF desalination plant. Besides, the diagnostic system performance is compared with the performances of two other diagnostic systems. The obtained results show some advantages for the BN based diagnostic system.Fil: Tarifa, Enrique Eduardo. Universidad Nacional de Jujuy; ArgentinaFil: Álvaro, F. Núñez. Universidad Nacional de Jujuy; ArgentinaFil: Franco, Samuel. Universidad Nacional de Jujuy; ArgentinaFil: Mussati, Sergio Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaDesalination2010-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/192855Tarifa, Enrique Eduardo; Álvaro, F. Núñez; Franco, Samuel; Mussati, Sergio Fabian; Fault diagnosis for an MSF desalination plant by using Bayesian networks; Desalination; Desalination and Water Treatment; 21; 1-3; 9-2010; 102-1081944-3994CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.5004/dwt.2010.1265info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.5004/dwt.2010.1265info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:46:13Zoai:ri.conicet.gov.ar:11336/192855instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:46:13.542CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Fault diagnosis for an MSF desalination plant by using Bayesian networks
title Fault diagnosis for an MSF desalination plant by using Bayesian networks
spellingShingle Fault diagnosis for an MSF desalination plant by using Bayesian networks
Tarifa, Enrique Eduardo
BAYESIAN NETWORKS
DYNAMIC SIMULATION
FAULT DIAGNOSIS
MSF DESALINATION PLANT
title_short Fault diagnosis for an MSF desalination plant by using Bayesian networks
title_full Fault diagnosis for an MSF desalination plant by using Bayesian networks
title_fullStr Fault diagnosis for an MSF desalination plant by using Bayesian networks
title_full_unstemmed Fault diagnosis for an MSF desalination plant by using Bayesian networks
title_sort Fault diagnosis for an MSF desalination plant by using Bayesian networks
dc.creator.none.fl_str_mv Tarifa, Enrique Eduardo
Álvaro, F. Núñez
Franco, Samuel
Mussati, Sergio Fabian
author Tarifa, Enrique Eduardo
author_facet Tarifa, Enrique Eduardo
Álvaro, F. Núñez
Franco, Samuel
Mussati, Sergio Fabian
author_role author
author2 Álvaro, F. Núñez
Franco, Samuel
Mussati, Sergio Fabian
author2_role author
author
author
dc.subject.none.fl_str_mv BAYESIAN NETWORKS
DYNAMIC SIMULATION
FAULT DIAGNOSIS
MSF DESALINATION PLANT
topic BAYESIAN NETWORKS
DYNAMIC SIMULATION
FAULT DIAGNOSIS
MSF DESALINATION PLANT
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) desalination plant by using BNs (Bayesian networks). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; i.e., it finds out which is the fault that is affecting the supervised process. A BN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. This paper determines both the structure and parameters of a BN intended for a diagnostic system. The implemented system is evaluated by using a dynamic simulator, which was developed for a real MSF desalination plant. Besides, the diagnostic system performance is compared with the performances of two other diagnostic systems. The obtained results show some advantages for the BN based diagnostic system.
Fil: Tarifa, Enrique Eduardo. Universidad Nacional de Jujuy; Argentina
Fil: Álvaro, F. Núñez. Universidad Nacional de Jujuy; Argentina
Fil: Franco, Samuel. Universidad Nacional de Jujuy; Argentina
Fil: Mussati, Sergio Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description This work outlines the development of a fault diagnostic system for an MSF (multi-stage flash) desalination plant by using BNs (Bayesian networks). This diagnostic system processes the plant data to determine whether the process state is normal or not. In the latter case, the diagnostic system determines the cause of the abnormal process state; i.e., it finds out which is the fault that is affecting the supervised process. A BN is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. A BN readily handles situations where some data entries are missing. This paper determines both the structure and parameters of a BN intended for a diagnostic system. The implemented system is evaluated by using a dynamic simulator, which was developed for a real MSF desalination plant. Besides, the diagnostic system performance is compared with the performances of two other diagnostic systems. The obtained results show some advantages for the BN based diagnostic system.
publishDate 2010
dc.date.none.fl_str_mv 2010-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/192855
Tarifa, Enrique Eduardo; Álvaro, F. Núñez; Franco, Samuel; Mussati, Sergio Fabian; Fault diagnosis for an MSF desalination plant by using Bayesian networks; Desalination; Desalination and Water Treatment; 21; 1-3; 9-2010; 102-108
1944-3994
CONICET Digital
CONICET
url http://hdl.handle.net/11336/192855
identifier_str_mv Tarifa, Enrique Eduardo; Álvaro, F. Núñez; Franco, Samuel; Mussati, Sergio Fabian; Fault diagnosis for an MSF desalination plant by using Bayesian networks; Desalination; Desalination and Water Treatment; 21; 1-3; 9-2010; 102-108
1944-3994
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.5004/dwt.2010.1265
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.5004/dwt.2010.1265
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Desalination
publisher.none.fl_str_mv Desalination
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842268778061103104
score 13.13397

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