A kernel-based approach for fault diagnosis in batch processes

This article explores the potential of kernel-based techniques for discriminating on-specification and off-specification batch runs, combining kernel-partial least squares discriminant analysis and three common approaches to analyze batch data by means of bilinear models: landmark features extractio...

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Detalles Bibliográficos
Autores: Vitale, R., de Noord, O. E., Ferrer, Alberto|||0000-0001-7244-5947
Tipo de recurso: artículo
Fecha de publicación:2014
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/60811
Acceso en línea:https://riunet.upv.es/handle/10251/60811
Access Level:acceso abierto
Palabra clave:Kernel-based methods
Pseudo-sample projection
Batch processes
Fault discrimination
Fault diagnosis
ESTADISTICA E INVESTIGACION OPERATIVA
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oai_identifier_str oai:riunet.upv.es:10251/60811
network_acronym_str ES
network_name_str España
repository_id_str
spelling A kernel-based approach for fault diagnosis in batch processesVitale, R.de Noord, O. E.Ferrer, Alberto|||0000-0001-7244-5947Kernel-based methodsPseudo-sample projectionBatch processesFault discriminationFault diagnosisESTADISTICA E INVESTIGACION OPERATIVAThis article explores the potential of kernel-based techniques for discriminating on-specification and off-specification batch runs, combining kernel-partial least squares discriminant analysis and three common approaches to analyze batch data by means of bilinear models: landmark features extraction, batchwise unfolding, and variablewise unfolding. Gower s idea of pseudo-sample projection is exploited to recover the contribution of the initial variables to the final model and visualize those having the highest discriminant power. The results show that the proposed approach provides an efficient fault discrimination and enables a correct identification of the discriminant variables in the considered case studies.WileyDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadEscuela Técnica Superior de Ingeniería IndustrialGrupo de Ingeniería Estadística Multivariante GIEMRepositorio Institucional de la Universitat Politècnica de València Riunet20142014-08-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/60811reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/608112026-06-13T07:49:27Z
dc.title.none.fl_str_mv A kernel-based approach for fault diagnosis in batch processes
title A kernel-based approach for fault diagnosis in batch processes
spellingShingle A kernel-based approach for fault diagnosis in batch processes
Vitale, R.
Kernel-based methods
Pseudo-sample projection
Batch processes
Fault discrimination
Fault diagnosis
ESTADISTICA E INVESTIGACION OPERATIVA
title_short A kernel-based approach for fault diagnosis in batch processes
title_full A kernel-based approach for fault diagnosis in batch processes
title_fullStr A kernel-based approach for fault diagnosis in batch processes
title_full_unstemmed A kernel-based approach for fault diagnosis in batch processes
title_sort A kernel-based approach for fault diagnosis in batch processes
dc.creator.none.fl_str_mv Vitale, R.
de Noord, O. E.
Ferrer, Alberto|||0000-0001-7244-5947
author Vitale, R.
author_facet Vitale, R.
de Noord, O. E.
Ferrer, Alberto|||0000-0001-7244-5947
author_role author
author2 de Noord, O. E.
Ferrer, Alberto|||0000-0001-7244-5947
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Escuela Técnica Superior de Ingeniería Industrial
Grupo de Ingeniería Estadística Multivariante GIEM
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Kernel-based methods
Pseudo-sample projection
Batch processes
Fault discrimination
Fault diagnosis
ESTADISTICA E INVESTIGACION OPERATIVA
topic Kernel-based methods
Pseudo-sample projection
Batch processes
Fault discrimination
Fault diagnosis
ESTADISTICA E INVESTIGACION OPERATIVA
description This article explores the potential of kernel-based techniques for discriminating on-specification and off-specification batch runs, combining kernel-partial least squares discriminant analysis and three common approaches to analyze batch data by means of bilinear models: landmark features extraction, batchwise unfolding, and variablewise unfolding. Gower s idea of pseudo-sample projection is exploited to recover the contribution of the initial variables to the final model and visualize those having the highest discriminant power. The results show that the proposed approach provides an efficient fault discrimination and enables a correct identification of the discriminant variables in the considered case studies.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-08-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/60811
url https://riunet.upv.es/handle/10251/60811
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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