A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space

Autores
Godoy, José Luis; Vega, Jorge Ruben; Marchetti, Jacinto Luis
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and diagnosis inmultivariate processes that exhibit collinear measurements. A typical PLS regression (PLSR) modeling strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLSdecomposition of the measurements into four terms that belongs to four different subspaces is derived. In order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped domains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the anomaly class from the observed pattern of the four component statistics with respect to their respective confidence intervals, and iii) identify the disturbed variables based on the analysis of the main variable contributions to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential application of this technique to real production systems
Fil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Marchetti, Jacinto Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Materia
Pls
Process Monitoring
Fault Detection
Fault Diagnosis
Fault Isolation
Contribution 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:11336/8877
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/8877
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement spaceGodoy, José LuisVega, Jorge RubenMarchetti, Jacinto LuisPlsProcess MonitoringFault DetectionFault DiagnosisFault IsolationContribution Analysishttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and diagnosis inmultivariate processes that exhibit collinear measurements. A typical PLS regression (PLSR) modeling strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLSdecomposition of the measurements into four terms that belongs to four different subspaces is derived. In order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped domains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the anomaly class from the observed pattern of the four component statistics with respect to their respective confidence intervals, and iii) identify the disturbed variables based on the analysis of the main variable contributions to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential application of this technique to real production systemsFil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Marchetti, Jacinto Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaElsevier Science2013-10info: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/11336/8877Godoy, José Luis; Vega, Jorge Ruben; Marchetti, Jacinto Luis; A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space; Elsevier Science; Chemometrics And Intelligent Laboratory Systems; 128; 10-2013; 25-360169-7439enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2013.07.006info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S016974391300138Xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:42:45Zoai:ri.conicet.gov.ar:11336/8877instacron: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:34982025-09-29 09:42:46.083CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
title A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
spellingShingle A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
Godoy, José Luis
Pls
Process Monitoring
Fault Detection
Fault Diagnosis
Fault Isolation
Contribution Analysis
title_short A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
title_full A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
title_fullStr A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
title_full_unstemmed A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
title_sort A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space
dc.creator.none.fl_str_mv Godoy, José Luis
Vega, Jorge Ruben
Marchetti, Jacinto Luis
author Godoy, José Luis
author_facet Godoy, José Luis
Vega, Jorge Ruben
Marchetti, Jacinto Luis
author_role author
author2 Vega, Jorge Ruben
Marchetti, Jacinto Luis
author2_role author
author
dc.subject.none.fl_str_mv Pls
Process Monitoring
Fault Detection
Fault Diagnosis
Fault Isolation
Contribution Analysis
topic Pls
Process Monitoring
Fault Detection
Fault Diagnosis
Fault Isolation
Contribution Analysis
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and diagnosis inmultivariate processes that exhibit collinear measurements. A typical PLS regression (PLSR) modeling strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLSdecomposition of the measurements into four terms that belongs to four different subspaces is derived. In order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped domains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the anomaly class from the observed pattern of the four component statistics with respect to their respective confidence intervals, and iii) identify the disturbed variables based on the analysis of the main variable contributions to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential application of this technique to real production systems
Fil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Marchetti, Jacinto Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
description A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and diagnosis inmultivariate processes that exhibit collinear measurements. A typical PLS regression (PLSR) modeling strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLSdecomposition of the measurements into four terms that belongs to four different subspaces is derived. In order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped domains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the anomaly class from the observed pattern of the four component statistics with respect to their respective confidence intervals, and iii) identify the disturbed variables based on the analysis of the main variable contributions to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential application of this technique to real production systems
publishDate 2013
dc.date.none.fl_str_mv 2013-10
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/11336/8877
Godoy, José Luis; Vega, Jorge Ruben; Marchetti, Jacinto Luis; A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space; Elsevier Science; Chemometrics And Intelligent Laboratory Systems; 128; 10-2013; 25-36
0169-7439
url http://hdl.handle.net/11336/8877
identifier_str_mv Godoy, José Luis; Vega, Jorge Ruben; Marchetti, Jacinto Luis; A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space; Elsevier Science; Chemometrics And Intelligent Laboratory Systems; 128; 10-2013; 25-36
0169-7439
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2013.07.006
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S016974391300138X
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
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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 13.070432

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