A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors

Autores
Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Robust Data Reconciliation enhances the quality of variable estimates when the data set contains a moderate proportion of atypical observations. But if systematic errors that persist in time, i.e. biases and drifts, are not detected, the break down point of the estimates is exceeded and results get worse. In this work, a new methodology based on the concepts of Robust Statistics is presented to deal with this problem. The strategy computes robust variable estimates, classifies the systematic measurement errors, and provides corrective actions to avoid the detrimental effect of biases and drifts until the sensor is repaired. The performance of the methodology is evaluated for the steady state operation of linear and non-linear benchmarks. Results demonstrate that its use significantly improves the estimates accuracy
Fil: Llanos, Claudia Elizabeth. 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: 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: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentina
Materia
Data Reconciliation
Robust Statistics
Measurement Errors
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:11336/43002
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation ErrorsLlanos, Claudia ElizabethSanchez, Mabel CristinaMaronna, Ricardo AntonioData ReconciliationRobust StatisticsMeasurement Errorshttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Robust Data Reconciliation enhances the quality of variable estimates when the data set contains a moderate proportion of atypical observations. But if systematic errors that persist in time, i.e. biases and drifts, are not detected, the break down point of the estimates is exceeded and results get worse. In this work, a new methodology based on the concepts of Robust Statistics is presented to deal with this problem. The strategy computes robust variable estimates, classifies the systematic measurement errors, and provides corrective actions to avoid the detrimental effect of biases and drifts until the sensor is repaired. The performance of the methodology is evaluated for the steady state operation of linear and non-linear benchmarks. Results demonstrate that its use significantly improves the estimates accuracyFil: Llanos, Claudia Elizabeth. 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: 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: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; ArgentinaElsevier Science2017-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/43002Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors; Elsevier Science; Computer Aided Chemical Engineering; 40; 7-2017; 1525-15301570-7946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/B978-0-444-63965-3.50256-7info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/B9780444639653502567info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:41:44Zoai:ri.conicet.gov.ar:11336/43002instacron: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 14:41:45.293CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
title A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
spellingShingle A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
Llanos, Claudia Elizabeth
Data Reconciliation
Robust Statistics
Measurement Errors
title_short A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
title_full A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
title_fullStr A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
title_full_unstemmed A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
title_sort A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
dc.creator.none.fl_str_mv Llanos, Claudia Elizabeth
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author Llanos, Claudia Elizabeth
author_facet Llanos, Claudia Elizabeth
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author_role author
author2 Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author2_role author
author
dc.subject.none.fl_str_mv Data Reconciliation
Robust Statistics
Measurement Errors
topic Data Reconciliation
Robust Statistics
Measurement Errors
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Robust Data Reconciliation enhances the quality of variable estimates when the data set contains a moderate proportion of atypical observations. But if systematic errors that persist in time, i.e. biases and drifts, are not detected, the break down point of the estimates is exceeded and results get worse. In this work, a new methodology based on the concepts of Robust Statistics is presented to deal with this problem. The strategy computes robust variable estimates, classifies the systematic measurement errors, and provides corrective actions to avoid the detrimental effect of biases and drifts until the sensor is repaired. The performance of the methodology is evaluated for the steady state operation of linear and non-linear benchmarks. Results demonstrate that its use significantly improves the estimates accuracy
Fil: Llanos, Claudia Elizabeth. 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: 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: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentina
description Robust Data Reconciliation enhances the quality of variable estimates when the data set contains a moderate proportion of atypical observations. But if systematic errors that persist in time, i.e. biases and drifts, are not detected, the break down point of the estimates is exceeded and results get worse. In this work, a new methodology based on the concepts of Robust Statistics is presented to deal with this problem. The strategy computes robust variable estimates, classifies the systematic measurement errors, and provides corrective actions to avoid the detrimental effect of biases and drifts until the sensor is repaired. The performance of the methodology is evaluated for the steady state operation of linear and non-linear benchmarks. Results demonstrate that its use significantly improves the estimates accuracy
publishDate 2017
dc.date.none.fl_str_mv 2017-07
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/11336/43002
Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors; Elsevier Science; Computer Aided Chemical Engineering; 40; 7-2017; 1525-1530
1570-7946
CONICET Digital
CONICET
url http://hdl.handle.net/11336/43002
identifier_str_mv Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors; Elsevier Science; Computer Aided Chemical Engineering; 40; 7-2017; 1525-1530
1570-7946
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/B978-0-444-63965-3.50256-7
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/B9780444639653502567
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
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 13.22299

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