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

Autores
Vega, Jorge Ruben; Godoy, José Luis; Marchetti, Jacinto
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and di- 24 agnosis inmultivariate processes that exhibit collinearmeasurements. A typical PLS regression (PLSR)modeling 25 strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLS- 26 decomposition of the measurements into four terms that belongs to four different subspaces is derived. In 27 Q2 order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped do- 28 mains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further 29 decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this 30 information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the 31 anomaly class from the observed pattern of the four component statistics with respect to their respective confi- 32 dence intervals, and iii) identify the disturbed variables based on the analysis of themain variable contributions 33 to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential appli- 34 cation of this technique to real production systems.
Fil: Vega, Jorge Ruben. Universidad Tecnológica Nacional. Argentina
Fil: Godoy, Jose Luis. Universidad Tecnológica Nacional. Argentina
Fil: Marchetti, Jacinto. Universidad Tecnológica Nacional. Argentina
Peer Reviewed
Materia
multivariate processes
PLS-decomposition
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/3121
Acceder
id RIAUTN_edd9051a3d7b021108b7cfa43eb4981b
oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/3121
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement spaceVega, Jorge RubenGodoy, José LuisMarchetti, Jacintomultivariate processesPLS-decompositionA newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and di- 24 agnosis inmultivariate processes that exhibit collinearmeasurements. A typical PLS regression (PLSR)modeling 25 strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLS- 26 decomposition of the measurements into four terms that belongs to four different subspaces is derived. In 27 Q2 order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped do- 28 mains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further 29 decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this 30 information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the 31 anomaly class from the observed pattern of the four component statistics with respect to their respective confi- 32 dence intervals, and iii) identify the disturbed variables based on the analysis of themain variable contributions 33 to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential appli- 34 cation of this technique to real production systems.Fil: Vega, Jorge Ruben. Universidad Tecnológica Nacional. ArgentinaFil: Godoy, Jose Luis. Universidad Tecnológica Nacional. ArgentinaFil: Marchetti, Jacinto. Universidad Tecnológica Nacional. ArgentinaPeer Reviewed2018-09-14T22:23:22Z2018-09-14T22:23:22Z2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/20.500.12272/3121enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Condiciones de Uso libre desde su aprobaciónAtribución-NoComercial-CompartirIgual 4.0 Internacionalreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2025-09-04T11:14:35Zoai:ria.utn.edu.ar:20.500.12272/3121instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2025-09-04 11:14:35.835Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
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
Vega, Jorge Ruben
multivariate processes
PLS-decomposition
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 Vega, Jorge Ruben
Godoy, José Luis
Marchetti, Jacinto
author Vega, Jorge Ruben
author_facet Vega, Jorge Ruben
Godoy, José Luis
Marchetti, Jacinto
author_role author
author2 Godoy, José Luis
Marchetti, Jacinto
author2_role author
author
dc.subject.none.fl_str_mv multivariate processes
PLS-decomposition
topic multivariate processes
PLS-decomposition
dc.description.none.fl_txt_mv A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and di- 24 agnosis inmultivariate processes that exhibit collinearmeasurements. A typical PLS regression (PLSR)modeling 25 strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLS- 26 decomposition of the measurements into four terms that belongs to four different subspaces is derived. In 27 Q2 order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped do- 28 mains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further 29 decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this 30 information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the 31 anomaly class from the observed pattern of the four component statistics with respect to their respective confi- 32 dence intervals, and iii) identify the disturbed variables based on the analysis of themain variable contributions 33 to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential appli- 34 cation of this technique to real production systems.
Fil: Vega, Jorge Ruben. Universidad Tecnológica Nacional. Argentina
Fil: Godoy, Jose Luis. Universidad Tecnológica Nacional. Argentina
Fil: Marchetti, Jacinto. Universidad Tecnológica Nacional. Argentina
Peer Reviewed
description A newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and di- 24 agnosis inmultivariate processes that exhibit collinearmeasurements. A typical PLS regression (PLSR)modeling 25 strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLS- 26 decomposition of the measurements into four terms that belongs to four different subspaces is derived. In 27 Q2 order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped do- 28 mains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further 29 decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this 30 information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the 31 anomaly class from the observed pattern of the four component statistics with respect to their respective confi- 32 dence intervals, and iii) identify the disturbed variables based on the analysis of themain variable contributions 33 to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential appli- 34 cation of this technique to real production systems.
publishDate 2013
dc.date.none.fl_str_mv 2013
2018-09-14T22:23:22Z
2018-09-14T22:23:22Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12272/3121
url http://hdl.handle.net/20.500.12272/3121
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Condiciones de Uso libre desde su aprobación
Atribución-NoComercial-CompartirIgual 4.0 Internacional
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Condiciones de Uso libre desde su aprobación
Atribución-NoComercial-CompartirIgual 4.0 Internacional
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 12.623145

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