Processing ambiguous fault signals with three models of feedforward neural networks

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 equi...

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Detalles Bibliográficos
Autores principales:	Martínez, Sergio, Franco Dominguez, Samuel, Tarifa, Enrique E.
Formato:	Objeto de conferencia
Lenguaje:	Español
Publicado:	2010
Materias:	Ciencias Informáticas Neural Networks Diagnosis Ambiguous Fault Signals Optimized training Signal processing
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/19381
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-19381
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Español
topic	Ciencias Informáticas Neural Networks Diagnosis Ambiguous Fault Signals Optimized training Signal processing
spellingShingle	Ciencias Informáticas Neural Networks Diagnosis Ambiguous Fault Signals Optimized training Signal processing Martínez, Sergio Franco Dominguez, Samuel Tarifa, Enrique E. Processing ambiguous fault signals with three models of feedforward neural networks
topic_facet	Ciencias Informáticas Neural Networks Diagnosis Ambiguous Fault Signals Optimized training Signal processing
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.
format	Objeto de conferencia Objeto de conferencia
author	Martínez, Sergio Franco Dominguez, Samuel Tarifa, Enrique E.
author_facet	Martínez, Sergio Franco Dominguez, Samuel Tarifa, Enrique E.
author_sort	Martínez, Sergio
title	Processing ambiguous fault signals with three models of feedforward neural networks
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
publishDate	2010
url	http://sedici.unlp.edu.ar/handle/10915/19381
work_keys_str_mv	AT martinezsergio processingambiguousfaultsignalswiththreemodelsoffeedforwardneuralnetworks AT francodominguezsamuel processingambiguousfaultsignalswiththreemodelsoffeedforwardneuralnetworks AT tarifaenriquee processingambiguousfaultsignalswiththreemodelsoffeedforwardneuralnetworks
bdutipo_str	Repositorios
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Processing ambiguous fault signals with three models of feedforward neural networks

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