A multivariate statistical process control procedure for BIAS identification in steady-state processes

Autores
Sanchez, Mabel Cristina; Alvarez Medina, Carlos Rodrigo; Brandolin, Adriana
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this article, a multivariate statistical process control (MSPC) strategy, devoted to bias identification and estimation for processes operating under steady-state conditions, is presented. The technique makes use of the D statistic to detect the presence of biases. Besides, it uses a new decomposition of this statistic to identify the faulty sensors. The strategy is based only on historical process data. Neither process modeling nor assumptions about the probability distribution of measurement errors are required. In contrast to methods based on fundamental models, both redundant and nonredundant measurements can be examined to identify the presence of biases. The performance of the proposed technique is evaluated using data-reconciliation benchmarks. Results indicate that the technique succeeds in identifying single and multiple biases and fulfills three paramount issues to practical implementation in commercial software: robustness, uncertainty, and efficiency. © 2008 American Institute of Chemical Engineers.
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Alvarez Medina, Carlos Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Brandolin, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Materia
Data Reconciliation
Statistical Analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/62418
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling A multivariate statistical process control procedure for BIAS identification in steady-state processesSanchez, Mabel CristinaAlvarez Medina, Carlos RodrigoBrandolin, AdrianaData ReconciliationStatistical Analysishttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2In this article, a multivariate statistical process control (MSPC) strategy, devoted to bias identification and estimation for processes operating under steady-state conditions, is presented. The technique makes use of the D statistic to detect the presence of biases. Besides, it uses a new decomposition of this statistic to identify the faulty sensors. The strategy is based only on historical process data. Neither process modeling nor assumptions about the probability distribution of measurement errors are required. In contrast to methods based on fundamental models, both redundant and nonredundant measurements can be examined to identify the presence of biases. The performance of the proposed technique is evaluated using data-reconciliation benchmarks. Results indicate that the technique succeeds in identifying single and multiple biases and fulfills three paramount issues to practical implementation in commercial software: robustness, uncertainty, and efficiency. © 2008 American Institute of Chemical Engineers.Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Alvarez Medina, Carlos Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Brandolin, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaJohn Wiley & Sons Inc2008-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/62418Sanchez, Mabel Cristina; Alvarez Medina, Carlos Rodrigo; Brandolin, Adriana; A multivariate statistical process control procedure for BIAS identification in steady-state processes; John Wiley & Sons Inc; Aiche Journal; 54; 8; 8-2008; 2082-20880001-1541CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1002/aic.11547info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/aic.11547info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:23:30Zoai:ri.conicet.gov.ar:11336/62418instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:23:30.675CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A multivariate statistical process control procedure for BIAS identification in steady-state processes
title A multivariate statistical process control procedure for BIAS identification in steady-state processes
spellingShingle A multivariate statistical process control procedure for BIAS identification in steady-state processes
Sanchez, Mabel Cristina
Data Reconciliation
Statistical Analysis
title_short A multivariate statistical process control procedure for BIAS identification in steady-state processes
title_full A multivariate statistical process control procedure for BIAS identification in steady-state processes
title_fullStr A multivariate statistical process control procedure for BIAS identification in steady-state processes
title_full_unstemmed A multivariate statistical process control procedure for BIAS identification in steady-state processes
title_sort A multivariate statistical process control procedure for BIAS identification in steady-state processes
dc.creator.none.fl_str_mv Sanchez, Mabel Cristina
Alvarez Medina, Carlos Rodrigo
Brandolin, Adriana
author Sanchez, Mabel Cristina
author_facet Sanchez, Mabel Cristina
Alvarez Medina, Carlos Rodrigo
Brandolin, Adriana
author_role author
author2 Alvarez Medina, Carlos Rodrigo
Brandolin, Adriana
author2_role author
author
dc.subject.none.fl_str_mv Data Reconciliation
Statistical Analysis
topic Data Reconciliation
Statistical Analysis
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this article, a multivariate statistical process control (MSPC) strategy, devoted to bias identification and estimation for processes operating under steady-state conditions, is presented. The technique makes use of the D statistic to detect the presence of biases. Besides, it uses a new decomposition of this statistic to identify the faulty sensors. The strategy is based only on historical process data. Neither process modeling nor assumptions about the probability distribution of measurement errors are required. In contrast to methods based on fundamental models, both redundant and nonredundant measurements can be examined to identify the presence of biases. The performance of the proposed technique is evaluated using data-reconciliation benchmarks. Results indicate that the technique succeeds in identifying single and multiple biases and fulfills three paramount issues to practical implementation in commercial software: robustness, uncertainty, and efficiency. © 2008 American Institute of Chemical Engineers.
Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Alvarez Medina, Carlos Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Brandolin, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
description In this article, a multivariate statistical process control (MSPC) strategy, devoted to bias identification and estimation for processes operating under steady-state conditions, is presented. The technique makes use of the D statistic to detect the presence of biases. Besides, it uses a new decomposition of this statistic to identify the faulty sensors. The strategy is based only on historical process data. Neither process modeling nor assumptions about the probability distribution of measurement errors are required. In contrast to methods based on fundamental models, both redundant and nonredundant measurements can be examined to identify the presence of biases. The performance of the proposed technique is evaluated using data-reconciliation benchmarks. Results indicate that the technique succeeds in identifying single and multiple biases and fulfills three paramount issues to practical implementation in commercial software: robustness, uncertainty, and efficiency. © 2008 American Institute of Chemical Engineers.
publishDate 2008
dc.date.none.fl_str_mv 2008-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/62418
Sanchez, Mabel Cristina; Alvarez Medina, Carlos Rodrigo; Brandolin, Adriana; A multivariate statistical process control procedure for BIAS identification in steady-state processes; John Wiley & Sons Inc; Aiche Journal; 54; 8; 8-2008; 2082-2088
0001-1541
CONICET Digital
CONICET
url http://hdl.handle.net/11336/62418
identifier_str_mv Sanchez, Mabel Cristina; Alvarez Medina, Carlos Rodrigo; Brandolin, Adriana; A multivariate statistical process control procedure for BIAS identification in steady-state processes; John Wiley & Sons Inc; Aiche Journal; 54; 8; 8-2008; 2082-2088
0001-1541
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/aic.11547
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/aic.11547
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons Inc
publisher.none.fl_str_mv John Wiley & Sons Inc
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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