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

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 robus...

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Autores principales: Llanos, Claudia Elizabeth, Sanchez, Mabel Cristina, Maronna, Ricardo Antonio
Formato: Articulo Preprint
Lenguaje:Inglés
Publicado: 2017
Materias:
Ciencias Exactas
Systematic measurement errors
Data reconciliation
Robust statistics
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/99948
https://ri.conicet.gov.ar/11336/43006
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-99948
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
Systematic measurement errors
Data reconciliation
Robust statistics
spellingShingle Ciencias Exactas
Systematic measurement errors
Data reconciliation
Robust statistics
Llanos, Claudia Elizabeth
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
topic_facet Ciencias Exactas
Systematic measurement errors
Data reconciliation
Robust statistics
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.
format Articulo
Preprint
author Llanos, Claudia Elizabeth
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author_facet Llanos, Claudia Elizabeth
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author_sort Llanos, Claudia Elizabeth
title Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
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
publishDate 2017
url http://sedici.unlp.edu.ar/handle/10915/99948
https://ri.conicet.gov.ar/11336/43006
work_keys_str_mv AT llanosclaudiaelizabeth classificationofsystematicmeasurementerrorswithintheframeworkofrobustdatareconciliation
AT sanchezmabelcristina classificationofsystematicmeasurementerrorswithintheframeworkofrobustdatareconciliation
AT maronnaricardoantonio classificationofsystematicmeasurementerrorswithintheframeworkofrobustdatareconciliation
bdutipo_str Repositorios
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