Processing ambiguous fault signals with three models of feedforward neural networks

Autores
Martínez, Sergio; Franco Dominguez, Samuel; Tarifa, Enrique E.
Año de publicación
2010
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In the industrial technological field, running equipment or processes usually is monitored through automatic diagnosis systems. Within several Technologies for implementing such systems, the artificial neuronal networks are the most successful and widely spread. The data signals coming from the equipments or processes under supervision are interpreted by the neuronal networks so as to diagnose the presence of any fault. In this work three models of artificial neural networks and two methods of training are analyzed so as to establish, based on real experiences, the best combination of the neuronal model and the training method for recognizing in an efficient way the ambiguous patterns of faults.
Presentado en el I Workshop Procesamiento de señales y Sistemas de Tiempo Real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Neural Networks
Diagnosis
Ambiguous Fault Signals
Optimized training
Signal processing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/19381
Acceder
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network_name_str SEDICI (UNLP)
spelling Processing ambiguous fault signals with three models of feedforward neural networksMartínez, SergioFranco Dominguez, SamuelTarifa, Enrique E.Ciencias InformáticasNeural NetworksDiagnosisAmbiguous Fault SignalsOptimized trainingSignal processingIn the industrial technological field, running equipment or processes usually is monitored through automatic diagnosis systems. Within several Technologies for implementing such systems, the artificial neuronal networks are the most successful and widely spread. The data signals coming from the equipments or processes under supervision are interpreted by the neuronal networks so as to diagnose the presence of any fault. In this work three models of artificial neural networks and two methods of training are analyzed so as to establish, based on real experiences, the best combination of the neuronal model and the training method for recognizing in an efficient way the ambiguous patterns of faults.Presentado en el I Workshop Procesamiento de señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI)2010-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1009-1019http://sedici.unlp.edu.ar/handle/10915/19381spainfo:eu-repo/semantics/altIdentifier/isbn/978-950-9474-49-9info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:35:26Zoai:sedici.unlp.edu.ar:10915/19381Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:35:26.923SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Processing ambiguous fault signals with three models of feedforward neural networks
title Processing ambiguous fault signals with three models of feedforward neural networks
spellingShingle Processing ambiguous fault signals with three models of feedforward neural networks
Martínez, Sergio
Ciencias Informáticas
Neural Networks
Diagnosis
Ambiguous Fault Signals
Optimized training
Signal processing
title_short Processing ambiguous fault signals with three models of feedforward neural networks
title_full Processing ambiguous fault signals with three models of feedforward neural networks
title_fullStr Processing ambiguous fault signals with three models of feedforward neural networks
title_full_unstemmed Processing ambiguous fault signals with three models of feedforward neural networks
title_sort Processing ambiguous fault signals with three models of feedforward neural networks
dc.creator.none.fl_str_mv Martínez, Sergio
Franco Dominguez, Samuel
Tarifa, Enrique E.
author Martínez, Sergio
author_facet Martínez, Sergio
Franco Dominguez, Samuel
Tarifa, Enrique E.
author_role author
author2 Franco Dominguez, Samuel
Tarifa, Enrique E.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Neural Networks
Diagnosis
Ambiguous Fault Signals
Optimized training
Signal processing
topic Ciencias Informáticas
Neural Networks
Diagnosis
Ambiguous Fault Signals
Optimized training
Signal processing
dc.description.none.fl_txt_mv In the industrial technological field, running equipment or processes usually is monitored through automatic diagnosis systems. Within several Technologies for implementing such systems, the artificial neuronal networks are the most successful and widely spread. The data signals coming from the equipments or processes under supervision are interpreted by the neuronal networks so as to diagnose the presence of any fault. In this work three models of artificial neural networks and two methods of training are analyzed so as to establish, based on real experiences, the best combination of the neuronal model and the training method for recognizing in an efficient way the ambiguous patterns of faults.
Presentado en el I Workshop Procesamiento de señales y Sistemas de Tiempo Real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)
description In the industrial technological field, running equipment or processes usually is monitored through automatic diagnosis systems. Within several Technologies for implementing such systems, the artificial neuronal networks are the most successful and widely spread. The data signals coming from the equipments or processes under supervision are interpreted by the neuronal networks so as to diagnose the presence of any fault. In this work three models of artificial neural networks and two methods of training are analyzed so as to establish, based on real experiences, the best combination of the neuronal model and the training method for recognizing in an efficient way the ambiguous patterns of faults.
publishDate 2010
dc.date.none.fl_str_mv 2010-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
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dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-9474-49-9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
1009-1019
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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