Show simple item record

dc.contributor.authorPourasgharlafmejani, Masoud
dc.contributor.authorCombastel, Christophe
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.authorOcampo-Martínez, Carlos
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2019-05-29T04:39:59Z
dc.date.issued2019-01-01
dc.identifier.citationPourasgharlafmejani, M. [et al.]. FD-ZKF: A Zonotopic Kalman Filter optimizing fault detection rather than state estimation. "Journal of process control", 1 Gener 2019, vol. 73, p. 89-102.
dc.identifier.issn0959-1524
dc.identifier.urihttp://hdl.handle.net/2117/133611
dc.description.abstractEnhancing the sensitivity to faults with respect to disturbances, rather than optimizing the precision of the state estimation using Kalman Filters (KF) is the subject of this paper. The stochastic paradigm (KF) is based on minimizing the trace of the state estimation error covariance. In the context of the bounded-error paradigm using Zonotopic Kalman Filters (ZKF), this is analog to minimize the covariation trace. From this analogy and keeping a similar observer-based structure as in ZKF, a criterion jointly inspired by set-membership approaches and approximate decoupling techniques coming from parity-space residual generation is proposed. Its on-line maximization provides an optimal time-varying observer gain leading to the so-called FD-ZKF filter that allows enhancing the fault detection properties. The characterization of minimum detectable fault magnitude is done based on a sensitivity analysis. The effect of the uncertainty is addressed using a set-membership approach and a zonotopic representation reducing set operations to simple matrix calculations. A case study based on a quadruple-tank system is used both to illustrate and compare the effectiveness of the results obtained from the FD-ZKF approach compared to a pure ZKF approach
dc.format.extent14 p.
dc.language.isoeng
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::Informàtica::Automàtica i control
dc.subject.lcshAutomatic control
dc.subject.lcshFailure analysis (Engineering)
dc.subject.otherUncertain systems
dc.subject.otherObservers
dc.subject.otherFault detection
dc.subject.otherBounded uncertainties
dc.subject.otherZonotopes
dc.subject.otherSensitivity analysis
dc.subject.otherMinimum detectable fault
dc.titleFD-ZKF: A Zonotopic Kalman Filter optimizing fault detection rather than state estimation
dc.typeArticle
dc.subject.lemacControl automàtic
dc.subject.lemacErrors de sistemes (Enginyeria)
dc.subject.lemacModels matemàtics
dc.subject.lemacMathematical models
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1016/j.jprocont.2018.12.003
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0959152418305377
dc.rights.accessRestricted access - publisher's policy
drac.iddocument23580117
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/CSIC/1P/DEOCS
dc.date.lift2021-01
upcommons.citation.authorPourasgharlafmejani, M.; Combastel, C.; Puig, V.; Ocampo-Martinez, C.A.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameJournal of process control
upcommons.citation.volume73
upcommons.citation.startingPage89
upcommons.citation.endingPage102


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 3.0 Spain