Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF

Autores
Khalil, M. I.
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper outlines and deals with the problem of fault detection, isolation and identification of the four-elements detector system attached to the Cairo Fourier diffractometer facility (CFDF) used for neutron time-of-flight (TOF) spectrum measurements. A feed forward neural network and error back propagation training algorithm are employed to diagnose four commonly occurring faults of the detector system: preamplifier, amplifier, discriminator and the high voltage. The diagnostic system processes the acquired data to determine whether the detector system state is normal or not. The experimental results showed that the trained network has the capability to detect and identify various faults which can make one of the detector units to be out of order.
Facultad de Informática
Materia
Ciencias Informáticas
Fault tolerance
Neural nets
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9681
Acceder
id SEDICI_cfc903833437457340385af5b91aa435
oai_identifier_str oai:sedici.unlp.edu.ar:10915/9681
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Neural Network based Fault Diagnosis Procedure for the Detector System of CFDFKhalil, M. I.Ciencias InformáticasFault toleranceNeural netsThis paper outlines and deals with the problem of fault detection, isolation and identification of the four-elements detector system attached to the Cairo Fourier diffractometer facility (CFDF) used for neutron time-of-flight (TOF) spectrum measurements. A feed forward neural network and error back propagation training algorithm are employed to diagnose four commonly occurring faults of the detector system: preamplifier, amplifier, discriminator and the high voltage. The diagnostic system processes the acquired data to determine whether the detector system state is normal or not. The experimental results showed that the trained network has the capability to detect and identify various faults which can make one of the detector units to be out of order.Facultad de Informática2010-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf137-142http://sedici.unlp.edu.ar/handle/10915/9681enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct10-5.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2026-02-26T10:38:33Zoai:sedici.unlp.edu.ar:10915/9681Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292026-02-26 10:38:34.091SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
spellingShingle Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
Khalil, M. I.
Ciencias Informáticas
Fault tolerance
Neural nets
title_short Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_full Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_fullStr Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_full_unstemmed Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_sort Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
dc.creator.none.fl_str_mv Khalil, M. I.
author Khalil, M. I.
author_facet Khalil, M. I.
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Fault tolerance
Neural nets
topic Ciencias Informáticas
Fault tolerance
Neural nets
dc.description.none.fl_txt_mv This paper outlines and deals with the problem of fault detection, isolation and identification of the four-elements detector system attached to the Cairo Fourier diffractometer facility (CFDF) used for neutron time-of-flight (TOF) spectrum measurements. A feed forward neural network and error back propagation training algorithm are employed to diagnose four commonly occurring faults of the detector system: preamplifier, amplifier, discriminator and the high voltage. The diagnostic system processes the acquired data to determine whether the detector system state is normal or not. The experimental results showed that the trained network has the capability to detect and identify various faults which can make one of the detector units to be out of order.
Facultad de Informática
description This paper outlines and deals with the problem of fault detection, isolation and identification of the four-elements detector system attached to the Cairo Fourier diffractometer facility (CFDF) used for neutron time-of-flight (TOF) spectrum measurements. A feed forward neural network and error back propagation training algorithm are employed to diagnose four commonly occurring faults of the detector system: preamplifier, amplifier, discriminator and the high voltage. The diagnostic system processes the acquired data to determine whether the detector system state is normal or not. The experimental results showed that the trained network has the capability to detect and identify various faults which can make one of the detector units to be out of order.
publishDate 2010
dc.date.none.fl_str_mv 2010-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/9681
url http://sedici.unlp.edu.ar/handle/10915/9681
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct10-5.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
137-142
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.176822

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