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    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/45594

    Título
    Fault detection based on time series modeling and multivariate statistical process control
    Autor
    Sánchez-Fernández, Alvar
    Baldán, Francisco Javier
    Sáinz Palmero, Gregorio IsmaelAutoridad UVA Orcid
    Benítez, José Manuel
    Fuente Aparicio, María Jesús de laAutoridad UVA Orcid
    Año del Documento
    2018
    Editorial
    Elsevier
    Descripción
    Producción Científica
    Documento Fuente
    Chemometrics and Intelligent Laboratory Systems, Noviembre 2018, vol. 182, p. 57–69
    Résumé
    Monitoring complex industrial plants is a very important task in order to ensure the management, reliability, safety and maintenance of the desired product quality. Early detection of abnormal events allows actions to prevent more serious consequences, improve the system's performance and reduce manufacturing costs. In this work, a new methodology for fault detection is introduced, based on time series models and statistical process control (MSPC). The proposal explicitly accounts for both dynamic and non-linearity properties of the system. A dynamic feature selection is carried out to interpret the dynamic relations by characterizing the auto- and crosscorrelations for every variable. After that, a time-series based model framework is used to obtain and validate the best descriptive model of the plant (either linear o non-linear). Fault detection is based on finding anomalies in the temporal residual signals obtained from the models by univariate and multivariate statistical process control charts. Finally, the performance of the method is validated on two benchmarks, a wastewater treatment plant and the Tennessee Eastman Plant. A comparison with other classical methods clearly demonstrates the over performance and feasibility of the proposed monitoring scheme.
    Palabras Clave
    Fault detection
    Dynamic feature selection
    Time-series modelling
    Statistical process control charts
    ISSN
    0169-7439
    Revisión por pares
    SI
    DOI
    10.1016/j.chemolab.2018.08.003
    Patrocinador
    Este trabajo forma parte del proyectos de investigación: MINECO-FEDER DPI2015-67341-C2-2- R, TIN2013-47210-P, TIN2016-81113-R
    Junata de Andalucia, con el proyecto P12-TIC-2958
    Version del Editor
    https://www.sciencedirect.com/science/article/abs/pii/S0169743918303459
    Propietario de los Derechos
    Elsevier
    Idioma
    eng
    URI
    http://uvadoc.uva.es/handle/10324/45594
    Tipo de versión
    info:eu-repo/semantics/submittedVersion
    Derechos
    restrictedAccess
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