New contributions to non linear process monitoring through kernel partial least squares

Autores
Vega, Jorge Ruben; Godoy, José Luis; Marchetti, Jacinto; Zumoffen, David
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
The kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to nonlinear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a nonlinear process. The effectiveness of the proposed methods is confirmed by using simulation examples. Keywords: KPLS Modeling, Fault Detection, Fault Diagnosis, Prediction Risk Assessment, Nonlinear Processes.
Fil: Vega, Jorge Ruben/ Universidad Tecnològica Nacional. Argentina
Peer Reviewed
Materia
nonlinear
process monitoring
Kernel Partial
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/3119
Acceder
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oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/3119
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling New contributions to non linear process monitoring through kernel partial least squaresVega, Jorge RubenGodoy, José LuisMarchetti, JacintoZumoffen, Davidnonlinearprocess monitoringKernel PartialThe kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to nonlinear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a nonlinear process. The effectiveness of the proposed methods is confirmed by using simulation examples. Keywords: KPLS Modeling, Fault Detection, Fault Diagnosis, Prediction Risk Assessment, Nonlinear Processes.Fil: Vega, Jorge Ruben/ Universidad Tecnològica Nacional. ArgentinaPeer Reviewed2018-09-14T22:08:31Z2018-09-14T22:08:31Z2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/20.500.12272/3119engTécnicas numéricas de estimación y optimización: aplicaciones en problemas de nanotecnologìa y de energía eléctrica,info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Condiciones de Uso libre desde su aprobación / aprobaciónAtribución-NoComercial-CompartirIgual 4.0 Internacionalreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2025-09-29T14:29:29Zoai:ria.utn.edu.ar:20.500.12272/3119instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2025-09-29 14:29:30.112Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv New contributions to non linear process monitoring through kernel partial least squares
title New contributions to non linear process monitoring through kernel partial least squares
spellingShingle New contributions to non linear process monitoring through kernel partial least squares
Vega, Jorge Ruben
nonlinear
process monitoring
Kernel Partial
title_short New contributions to non linear process monitoring through kernel partial least squares
title_full New contributions to non linear process monitoring through kernel partial least squares
title_fullStr New contributions to non linear process monitoring through kernel partial least squares
title_full_unstemmed New contributions to non linear process monitoring through kernel partial least squares
title_sort New contributions to non linear process monitoring through kernel partial least squares
dc.creator.none.fl_str_mv Vega, Jorge Ruben
Godoy, José Luis
Marchetti, Jacinto
Zumoffen, David
author Vega, Jorge Ruben
author_facet Vega, Jorge Ruben
Godoy, José Luis
Marchetti, Jacinto
Zumoffen, David
author_role author
author2 Godoy, José Luis
Marchetti, Jacinto
Zumoffen, David
author2_role author
author
author
dc.subject.none.fl_str_mv nonlinear
process monitoring
Kernel Partial
topic nonlinear
process monitoring
Kernel Partial
dc.description.none.fl_txt_mv The kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to nonlinear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a nonlinear process. The effectiveness of the proposed methods is confirmed by using simulation examples. Keywords: KPLS Modeling, Fault Detection, Fault Diagnosis, Prediction Risk Assessment, Nonlinear Processes.
Fil: Vega, Jorge Ruben/ Universidad Tecnològica Nacional. Argentina
Peer Reviewed
description The kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to nonlinear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a nonlinear process. The effectiveness of the proposed methods is confirmed by using simulation examples. Keywords: KPLS Modeling, Fault Detection, Fault Diagnosis, Prediction Risk Assessment, Nonlinear Processes.
publishDate 2013
dc.date.none.fl_str_mv 2013
2018-09-14T22:08:31Z
2018-09-14T22:08:31Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12272/3119
url http://hdl.handle.net/20.500.12272/3119
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Técnicas numéricas de estimación y optimización: aplicaciones en problemas de nanotecnologìa y de energía eléctrica,
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Condiciones de Uso libre desde su aprobación / aprobación
Atribución-NoComercial-CompartirIgual 4.0 Internacional
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Condiciones de Uso libre desde su aprobación / aprobación
Atribución-NoComercial-CompartirIgual 4.0 Internacional
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 12.559606

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