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dc.contributor.authorShokry, Ahmed
dc.contributor.authorArdakani, Mohammadhamed
dc.contributor.authorEscudero Bakx, Gerard
dc.contributor.authorGraells Sobré, Moisès
dc.contributor.authorEspuña Camarasa, Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Química
dc.date.accessioned2017-03-21T13:25:17Z
dc.date.available2017-03-21T13:25:17Z
dc.date.issued2016
dc.identifier.citationShokry , A., Ardakani, M., Escudero, G., Graells, M., Espuña, A. Kriging based fault detection and diagnosis approach for nonlinear noisy dynamic processes. A: European Symposium on Computer Aided Process Engineering. "26th Symposium on Computer Aided Process Engineering". Portorož: Elsevier, 2016, p. 55-60.
dc.identifier.isbn978-0-444-63873-1
dc.identifier.urihttp://hdl.handle.net/2117/102743
dc.description.abstractThis paper presents a hybrid approach to enhance the performance of the data-based Pattern Classification Techniques (PCTs) used for Fault Detection and Diagnosis (FDD) of nonlinear dynamic noisy processes. The method combines kriging metamodels with PCT (e.g. Support Vector Machines). The metamodels are used in two different ways; first, they are used as Multivariate Dynamic Kriging(s) (MDKs) which estimate the process dynamic behavior/outputs, second, as classical static models which are used for smoothing noise and imputing missing values of the process actual outputs measurements. So during the process operations, the estimated and the smoothed actual outputs are compared, and residual/error signals are generated that is used by the classifier to detect and diagnose the process possible faults. The method is applied to a benchmark case study, showing a high enhancement in such PCTs due to the introduction of the process dynamics information to these PCTs via the MDKs, and by smoothing the noise and imputing the missing measurements using the static kriging.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria química
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshAutomatic control
dc.subject.otherHybrid Fault Detection and Diagnosis
dc.subject.otherPattern Classification Techniques
dc.subject.otherMultivariate Dynamic Kriging
dc.subject.otherDynamic Modelling.
dc.titleKriging based fault detection and diagnosis approach for nonlinear noisy dynamic processes
dc.typeConference report
dc.subject.lemacControl automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.contributor.groupUniversitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering
dc.identifier.doihttp://dx.doi.org/10.1016/B978-0-444-63428-3.50014-X
dc.rights.accessRestricted access - publisher's policy
drac.iddocument19707984
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorShokry , A., Ardakani, M., Escudero, G., Graells, M., Espuña, A.
upcommons.citation.contributorEuropean Symposium on Computer Aided Process Engineering
upcommons.citation.pubplacePortorož
upcommons.citation.publishedtrue
upcommons.citation.publicationName26th Symposium on Computer Aided Process Engineering
upcommons.citation.startingPage55
upcommons.citation.endingPage60


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