A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes

Autores
Maestri, Mauricio Leonardo; Farall, Rodolfo Andres; Groisman, Pablo Jose; Cassanello, Miryan; Horowitz, Gabriel Ignacio
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Many classical multivariate statistical process monitoring (MSPM) techniques assume normal distribution of the data and independence of the samples. Very often, these assumptions do not hold for real industrial chemical processes, where multiple plant operating modes lead to multiple nominal operation regions. MSPM techniques that do not take account of this fact show increased false alarm and missing alarm rates. In this work, a simple fault detection tool based on a robust clustering technique is implemented to detect abnormal situations in an industrial installation with multiple operation modes. The tool is applied to three case studies: (i) a two-dimensional toy example, (ii) a realistic simulation usually used as a benchmark example, known as the Tennessee?Eastman Process, and (iii) real data from a methanol plant. The clustering technique on which the tool relies assumes that the observations come from multiple populations with a common covariance matrix (i.e., the same underlying physical relations). The clustering technique is also capable of coping with a certain percentage of outliers, thus avoiding the need of extensive preprocessing of the data. Moreover, improvements in detection capacity are found when comparing the results to those obtained with standard methodologies. Hence, the feasibility of implementing fault detection tools based on this technique in the field of chemical industrial processes is discussed.
Fil: Maestri, Mauricio Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; Argentina
Fil: Farall, Rodolfo Andres. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina
Fil: Groisman, Pablo Jose. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina
Fil: Cassanello, Miryan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; Argentina
Fil: Horowitz, Gabriel Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; Argentina. YPF - Tecnología; Argentina
Materia
Fault detection
Multiple operating modes
Multivariate statistical process monitoring
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/248371
Acceder
id CONICETDig_f132778f1acec7eb604951de7f363c08
oai_identifier_str oai:ri.conicet.gov.ar:11336/248371
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modesMaestri, Mauricio LeonardoFarall, Rodolfo AndresGroisman, Pablo JoseCassanello, MiryanHorowitz, Gabriel IgnacioFault detectionMultiple operating modesMultivariate statistical process monitoringhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Many classical multivariate statistical process monitoring (MSPM) techniques assume normal distribution of the data and independence of the samples. Very often, these assumptions do not hold for real industrial chemical processes, where multiple plant operating modes lead to multiple nominal operation regions. MSPM techniques that do not take account of this fact show increased false alarm and missing alarm rates. In this work, a simple fault detection tool based on a robust clustering technique is implemented to detect abnormal situations in an industrial installation with multiple operation modes. The tool is applied to three case studies: (i) a two-dimensional toy example, (ii) a realistic simulation usually used as a benchmark example, known as the Tennessee?Eastman Process, and (iii) real data from a methanol plant. The clustering technique on which the tool relies assumes that the observations come from multiple populations with a common covariance matrix (i.e., the same underlying physical relations). The clustering technique is also capable of coping with a certain percentage of outliers, thus avoiding the need of extensive preprocessing of the data. Moreover, improvements in detection capacity are found when comparing the results to those obtained with standard methodologies. Hence, the feasibility of implementing fault detection tools based on this technique in the field of chemical industrial processes is discussed.Fil: Maestri, Mauricio Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; ArgentinaFil: Farall, Rodolfo Andres. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; ArgentinaFil: Groisman, Pablo Jose. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; ArgentinaFil: Cassanello, Miryan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; ArgentinaFil: Horowitz, Gabriel Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; Argentina. YPF - Tecnología; ArgentinaPergamon-Elsevier Science Ltd2010-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/248371Maestri, Mauricio Leonardo; Farall, Rodolfo Andres; Groisman, Pablo Jose; Cassanello, Miryan; Horowitz, Gabriel Ignacio; A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 34; 2; 3-2010; 223-2310098-1354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0098135409001331info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2009.05.012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:23:46Zoai:ri.conicet.gov.ar:11336/248371instacron: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 15:23:46.266CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
title A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
spellingShingle A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
Maestri, Mauricio Leonardo
Fault detection
Multiple operating modes
Multivariate statistical process monitoring
title_short A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
title_full A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
title_fullStr A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
title_full_unstemmed A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
title_sort A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes
dc.creator.none.fl_str_mv Maestri, Mauricio Leonardo
Farall, Rodolfo Andres
Groisman, Pablo Jose
Cassanello, Miryan
Horowitz, Gabriel Ignacio
author Maestri, Mauricio Leonardo
author_facet Maestri, Mauricio Leonardo
Farall, Rodolfo Andres
Groisman, Pablo Jose
Cassanello, Miryan
Horowitz, Gabriel Ignacio
author_role author
author2 Farall, Rodolfo Andres
Groisman, Pablo Jose
Cassanello, Miryan
Horowitz, Gabriel Ignacio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Fault detection
Multiple operating modes
Multivariate statistical process monitoring
topic Fault detection
Multiple operating modes
Multivariate statistical process monitoring
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Many classical multivariate statistical process monitoring (MSPM) techniques assume normal distribution of the data and independence of the samples. Very often, these assumptions do not hold for real industrial chemical processes, where multiple plant operating modes lead to multiple nominal operation regions. MSPM techniques that do not take account of this fact show increased false alarm and missing alarm rates. In this work, a simple fault detection tool based on a robust clustering technique is implemented to detect abnormal situations in an industrial installation with multiple operation modes. The tool is applied to three case studies: (i) a two-dimensional toy example, (ii) a realistic simulation usually used as a benchmark example, known as the Tennessee?Eastman Process, and (iii) real data from a methanol plant. The clustering technique on which the tool relies assumes that the observations come from multiple populations with a common covariance matrix (i.e., the same underlying physical relations). The clustering technique is also capable of coping with a certain percentage of outliers, thus avoiding the need of extensive preprocessing of the data. Moreover, improvements in detection capacity are found when comparing the results to those obtained with standard methodologies. Hence, the feasibility of implementing fault detection tools based on this technique in the field of chemical industrial processes is discussed.
Fil: Maestri, Mauricio Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; Argentina
Fil: Farall, Rodolfo Andres. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina
Fil: Groisman, Pablo Jose. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina
Fil: Cassanello, Miryan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; Argentina
Fil: Horowitz, Gabriel Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias; Argentina. YPF - Tecnología; Argentina
description Many classical multivariate statistical process monitoring (MSPM) techniques assume normal distribution of the data and independence of the samples. Very often, these assumptions do not hold for real industrial chemical processes, where multiple plant operating modes lead to multiple nominal operation regions. MSPM techniques that do not take account of this fact show increased false alarm and missing alarm rates. In this work, a simple fault detection tool based on a robust clustering technique is implemented to detect abnormal situations in an industrial installation with multiple operation modes. The tool is applied to three case studies: (i) a two-dimensional toy example, (ii) a realistic simulation usually used as a benchmark example, known as the Tennessee?Eastman Process, and (iii) real data from a methanol plant. The clustering technique on which the tool relies assumes that the observations come from multiple populations with a common covariance matrix (i.e., the same underlying physical relations). The clustering technique is also capable of coping with a certain percentage of outliers, thus avoiding the need of extensive preprocessing of the data. Moreover, improvements in detection capacity are found when comparing the results to those obtained with standard methodologies. Hence, the feasibility of implementing fault detection tools based on this technique in the field of chemical industrial processes is discussed.
publishDate 2010
dc.date.none.fl_str_mv 2010-03
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/11336/248371
Maestri, Mauricio Leonardo; Farall, Rodolfo Andres; Groisman, Pablo Jose; Cassanello, Miryan; Horowitz, Gabriel Ignacio; A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 34; 2; 3-2010; 223-231
0098-1354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/248371
identifier_str_mv Maestri, Mauricio Leonardo; Farall, Rodolfo Andres; Groisman, Pablo Jose; Cassanello, Miryan; Horowitz, Gabriel Ignacio; A robust clustering method for detection of abnormal situations in a process with multiple steady-state operation modes; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 34; 2; 3-2010; 223-231
0098-1354
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0098135409001331
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2009.05.012
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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
_version_ 1846083384706596864
score 13.22299

Publicaciones similares