Defining the structure of DPCA models and its impact on process monitoring and prediction activities
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/27235 https://doi.org/10.1016/j.chemolab.2013.03.009 |
Resumo: | Dynamic Principal Component Analysis (DPCA) is an extension of Principal Component Analysis (PCA), developed in order to add the ability to capture the autocorrelative behavior of processes, to the existent and well-known PCA capability for modeling cross-correlation between variables. The simultaneous modeling of the dependencies along the “variable” and “time” modes, allows for a more compact and rigorous description of the normal behavior of processes, laying the ground for the development of, for instance, improved Statistical Process Monitoring (SPM) methodologies, able to robustly detect finer deviations from normal operation conditions. A key point in the application of DPCA is the definition of its structure, namely the selection of the number of time-shifted replicates for each variable to include, and the number of components to retain in the final model. In order to address the first of these two fundamental design aspects of DPCA, and arguably the most complex one, we propose two new lag selection methods.The first method estimates a single lag structure for all variables, whereas the second one refines this procedure, providing the specific number of lags to be used for each individual variable. The application of these two proposed methodologies to several case studies led to a more rigorous estimation of the number of lags really involved in the dynamical mechanisms of the processes under analysis. This feature can be explored for implementing improved system identification, process monitoring and process control tasks that rely upon a DPCA modeling framework. |
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Defining the structure of DPCA models and its impact on process monitoring and prediction activitiesLag selectionDynamic principal component analysis (DPCA)Multivariate statistical process control (MSPC)System identificationDynamic Principal Component Analysis (DPCA) is an extension of Principal Component Analysis (PCA), developed in order to add the ability to capture the autocorrelative behavior of processes, to the existent and well-known PCA capability for modeling cross-correlation between variables. The simultaneous modeling of the dependencies along the “variable” and “time” modes, allows for a more compact and rigorous description of the normal behavior of processes, laying the ground for the development of, for instance, improved Statistical Process Monitoring (SPM) methodologies, able to robustly detect finer deviations from normal operation conditions. A key point in the application of DPCA is the definition of its structure, namely the selection of the number of time-shifted replicates for each variable to include, and the number of components to retain in the final model. In order to address the first of these two fundamental design aspects of DPCA, and arguably the most complex one, we propose two new lag selection methods.The first method estimates a single lag structure for all variables, whereas the second one refines this procedure, providing the specific number of lags to be used for each individual variable. The application of these two proposed methodologies to several case studies led to a more rigorous estimation of the number of lags really involved in the dynamical mechanisms of the processes under analysis. This feature can be explored for implementing improved system identification, process monitoring and process control tasks that rely upon a DPCA modeling framework.Elsevier2013-06-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/27235http://hdl.handle.net/10316/27235https://doi.org/10.1016/j.chemolab.2013.03.009porRATO, Tiago J.; REIS, Marco S. - Defining the structure of DPCA models and its impact on process monitoring and prediction activities. "Chemometrics and Intelligent Laboratory Systems". ISSN 0169-7439. Vol. 125 (2013) p. 74-860169-7439http://www.sciencedirect.com/science/article/pii/S016974391300049XRato, Tiago J.Reis, Marco S.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2020-05-29T10:04:40Zoai:estudogeral.uc.pt:10316/27235Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:01:49.927173Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Defining the structure of DPCA models and its impact on process monitoring and prediction activities |
title |
Defining the structure of DPCA models and its impact on process monitoring and prediction activities |
spellingShingle |
Defining the structure of DPCA models and its impact on process monitoring and prediction activities Rato, Tiago J. Lag selection Dynamic principal component analysis (DPCA) Multivariate statistical process control (MSPC) System identification |
title_short |
Defining the structure of DPCA models and its impact on process monitoring and prediction activities |
title_full |
Defining the structure of DPCA models and its impact on process monitoring and prediction activities |
title_fullStr |
Defining the structure of DPCA models and its impact on process monitoring and prediction activities |
title_full_unstemmed |
Defining the structure of DPCA models and its impact on process monitoring and prediction activities |
title_sort |
Defining the structure of DPCA models and its impact on process monitoring and prediction activities |
author |
Rato, Tiago J. |
author_facet |
Rato, Tiago J. Reis, Marco S. |
author_role |
author |
author2 |
Reis, Marco S. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Rato, Tiago J. Reis, Marco S. |
dc.subject.por.fl_str_mv |
Lag selection Dynamic principal component analysis (DPCA) Multivariate statistical process control (MSPC) System identification |
topic |
Lag selection Dynamic principal component analysis (DPCA) Multivariate statistical process control (MSPC) System identification |
description |
Dynamic Principal Component Analysis (DPCA) is an extension of Principal Component Analysis (PCA), developed in order to add the ability to capture the autocorrelative behavior of processes, to the existent and well-known PCA capability for modeling cross-correlation between variables. The simultaneous modeling of the dependencies along the “variable” and “time” modes, allows for a more compact and rigorous description of the normal behavior of processes, laying the ground for the development of, for instance, improved Statistical Process Monitoring (SPM) methodologies, able to robustly detect finer deviations from normal operation conditions. A key point in the application of DPCA is the definition of its structure, namely the selection of the number of time-shifted replicates for each variable to include, and the number of components to retain in the final model. In order to address the first of these two fundamental design aspects of DPCA, and arguably the most complex one, we propose two new lag selection methods.The first method estimates a single lag structure for all variables, whereas the second one refines this procedure, providing the specific number of lags to be used for each individual variable. The application of these two proposed methodologies to several case studies led to a more rigorous estimation of the number of lags really involved in the dynamical mechanisms of the processes under analysis. This feature can be explored for implementing improved system identification, process monitoring and process control tasks that rely upon a DPCA modeling framework. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-06-15 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/27235 http://hdl.handle.net/10316/27235 https://doi.org/10.1016/j.chemolab.2013.03.009 |
url |
http://hdl.handle.net/10316/27235 https://doi.org/10.1016/j.chemolab.2013.03.009 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
RATO, Tiago J.; REIS, Marco S. - Defining the structure of DPCA models and its impact on process monitoring and prediction activities. "Chemometrics and Intelligent Laboratory Systems". ISSN 0169-7439. Vol. 125 (2013) p. 74-86 0169-7439 http://www.sciencedirect.com/science/article/pii/S016974391300049X |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133908826062848 |