Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation

Autores
Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.
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
SYSTEMATIC MEASUREMENT ERRORS
DATA RECONCILIATION
ROBUST STATISTICS
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:11336/43006
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Classification of Systematic Measurement Errors within the Framework of Robust Data ReconciliationLlanos, Claudia ElizabethSanchez, Mabel CristinaMaronna, Ricardo AntonioSYSTEMATIC MEASUREMENT ERRORSDATA RECONCILIATIONROBUST STATISTICShttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.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; 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; ArgentinaAmerican Chemical Society2017-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/43006Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation; American Chemical Society; Industrial & Engineering Chemical Research; 56; 34; 7-2017; 9617-96280888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.iecr.7b00726info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.iecr.7b00726info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:57:26Zoai:ri.conicet.gov.ar:11336/43006instacron: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 14:57:27.209CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
title Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
spellingShingle Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
Llanos, Claudia Elizabeth
SYSTEMATIC MEASUREMENT ERRORS
DATA RECONCILIATION
ROBUST STATISTICS
title_short Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
title_full Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
title_fullStr Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
title_full_unstemmed Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
title_sort Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
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 SYSTEMATIC MEASUREMENT ERRORS
DATA RECONCILIATION
ROBUST STATISTICS
topic SYSTEMATIC MEASUREMENT ERRORS
DATA RECONCILIATION
ROBUST STATISTICS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.
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 A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.
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/11336/43006
Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation; American Chemical Society; Industrial & Engineering Chemical Research; 56; 34; 7-2017; 9617-9628
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/43006
identifier_str_mv Llanos, Claudia Elizabeth; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation; American Chemical Society; Industrial & Engineering Chemical Research; 56; 34; 7-2017; 9617-9628
0888-5885
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://pubs.acs.org/doi/10.1021/acs.iecr.7b00726
info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.iecr.7b00726
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 American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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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