Multivariate SPC via sequential multiblock-PLS

[EN] The sequential multi-block partial least squares (SMB-PLS) is proposed for implementing a multivariate statistical process control scheme. This is of interest when the system is composed of several blocks following a sequential order and presenting correlated information, for instance, a raw ma...

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Detalles Bibliográficos
Autores: Borràs-Ferrís, Joan, Duchesne, Carl, Ferrer, Alberto|||0000-0001-7244-5947
Tipo de recurso: artículo
Fecha de publicación:2024
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:10251/213534
Acceso en línea:https://riunet.upv.es/handle/10251/213534
Access Level:acceso abierto
Palabra clave:Multivariate statistical process control (MSPC)
Sequential multiblock modelling
Partial least squares (PLS)
Raw material properties
ESTADISTICA E INVESTIGACION OPERATIVA
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spelling Multivariate SPC via sequential multiblock-PLSBorràs-Ferrís, JoanDuchesne, CarlFerrer, Alberto|||0000-0001-7244-5947Multivariate statistical process control (MSPC)Sequential multiblock modellingPartial least squares (PLS)Raw material propertiesESTADISTICA E INVESTIGACION OPERATIVA[EN] The sequential multi-block partial least squares (SMB-PLS) is proposed for implementing a multivariate statistical process control scheme. This is of interest when the system is composed of several blocks following a sequential order and presenting correlated information, for instance, a raw material properties block followed by a process variables block that is manipulated according to raw material properties. The SMB-PLS uses orthogonalization to separate correlated information between blocks from orthogonal variations. This allows monitoring the system in different stages considering only the remaining orthogonal part in each block. Thus, the SMB-PLS increases the interpretability and process understanding in the model building (Phase I), since it provides a deep insight about the nature of the system variations. Besides, it prevents any special cause from propagating to subsequent blocks enabling their use in the model exploitation (Phase II). The methodology is applied to a real case study from a food manufacturing process.AcknowledgementsThis work was partially supported by the Natural Council of Canada (NSERC) [RGPIN-2019-04800] and by the Spanish Ministry of Science and Innovation (PID2020-119262RB-I00)ElsevierDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadEscuela Técnica Superior de Ingeniería IndustrialGrupo de Ingeniería Estadística Multivariante GIEMAgencia Estatal de InvestigaciónNatural Sciences and Engineering Research Council of CanadaUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-11-15journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/213534reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-119262RB-I00 TECNICAS ESTADISTICAS MULTIVARIANTES BASADAS EN VARIABLES LATENTES PARA EL DESARROLLO DE BIOMARCADORES DE IMAGEN PARA LA DIAGNOSIS Y PROGNOSIS DE CANCER DE MAMANatural Sciences and Engineering Research Council of Canada NSERC RGPIN-2019-04800open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2135342026-06-13T07:49:27Z
dc.title.none.fl_str_mv Multivariate SPC via sequential multiblock-PLS
title Multivariate SPC via sequential multiblock-PLS
spellingShingle Multivariate SPC via sequential multiblock-PLS
Borràs-Ferrís, Joan
Multivariate statistical process control (MSPC)
Sequential multiblock modelling
Partial least squares (PLS)
Raw material properties
ESTADISTICA E INVESTIGACION OPERATIVA
title_short Multivariate SPC via sequential multiblock-PLS
title_full Multivariate SPC via sequential multiblock-PLS
title_fullStr Multivariate SPC via sequential multiblock-PLS
title_full_unstemmed Multivariate SPC via sequential multiblock-PLS
title_sort Multivariate SPC via sequential multiblock-PLS
dc.creator.none.fl_str_mv Borràs-Ferrís, Joan
Duchesne, Carl
Ferrer, Alberto|||0000-0001-7244-5947
author Borràs-Ferrís, Joan
author_facet Borràs-Ferrís, Joan
Duchesne, Carl
Ferrer, Alberto|||0000-0001-7244-5947
author_role author
author2 Duchesne, Carl
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
Agencia Estatal de Investigación
Natural Sciences and Engineering Research Council of Canada
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Multivariate statistical process control (MSPC)
Sequential multiblock modelling
Partial least squares (PLS)
Raw material properties
ESTADISTICA E INVESTIGACION OPERATIVA
topic Multivariate statistical process control (MSPC)
Sequential multiblock modelling
Partial least squares (PLS)
Raw material properties
ESTADISTICA E INVESTIGACION OPERATIVA
description [EN] The sequential multi-block partial least squares (SMB-PLS) is proposed for implementing a multivariate statistical process control scheme. This is of interest when the system is composed of several blocks following a sequential order and presenting correlated information, for instance, a raw material properties block followed by a process variables block that is manipulated according to raw material properties. The SMB-PLS uses orthogonalization to separate correlated information between blocks from orthogonal variations. This allows monitoring the system in different stages considering only the remaining orthogonal part in each block. Thus, the SMB-PLS increases the interpretability and process understanding in the model building (Phase I), since it provides a deep insight about the nature of the system variations. Besides, it prevents any special cause from propagating to subsequent blocks enabling their use in the model exploitation (Phase II). The methodology is applied to a real case study from a food manufacturing process.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-11-15
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/10251/213534
url https://riunet.upv.es/handle/10251/213534
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-119262RB-I00 TECNICAS ESTADISTICAS MULTIVARIANTES BASADAS EN VARIABLES LATENTES PARA EL DESARROLLO DE BIOMARCADORES DE IMAGEN PARA LA DIAGNOSIS Y PROGNOSIS DE CANCER DE MAMA
Natural Sciences and Engineering Research Council of Canada NSERC RGPIN-2019-04800
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
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
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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 15.812429

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