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dc.contributor.authorAkielaszek-Witczak, Anna
dc.contributor.authorMrugalska, Beata
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.authorWyrwicka, Magdalena
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2016-02-11T07:55:33Z
dc.date.issued2015
dc.identifier.citationAkielaszek-Witczak, A., Mrugalska, B., Puig, V., Wyrwicka, M. Robust neural-network-based fault detection with sequential D-optimum bounded-error input design. A: IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. "IFAC-PapersOnLine (volume 48, issue 21, Pages 1-1496): 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 Paris, 2–4 September 2015". París: International Federation of Automatic Control (IFAC), 2015, p. 434-439.
dc.identifier.isbn2405-8963
dc.identifier.urihttp://hdl.handle.net/2117/82806
dc.description.abstractA growing demand for technologically advanced systems has contributed to the increase of the awareness of systems safety and reliability. Such a situation requires the development of novel methods of robust fault diagnosis. The application of the analytical redundancy based methods for system fault detection causes that theIr effectiveness depends on model quality. In this paper, a new Methodology for the improvement of the neural model with a D-optimum sequential experimental design technique combined with outer bounding ellipsoid algorithm is proposed. Moreover, a novel method of robust fault detection against neural model uncertainty and disturbances is developed. Such an approach is used for modelling and robust fault detection of the three-screw spindle oil pump.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherInternational Federation of Automatic Control (IFAC)
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshRobust control
dc.subject.otherNeural networks
dc.subject.othersystem identification
dc.subject.otheroptimum experiment design
dc.subject.otherfault detection
dc.subject.otherrobustness
dc.subject.otherbounded disturbances
dc.titleRobust neural-network-based fault detection with sequential D-optimum bounded-error input design
dc.typeConference report
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacControl de robustesa
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1016/j.ifacol.2015.09.565
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac17405267
dc.description.versionPostprint (author's final draft)
dc.date.lift10000-01-01
local.citation.authorAkielaszek-Witczak, A.; Mrugalska, B.; Puig, V.; Wyrwicka, M.
local.citation.contributorIFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes
local.citation.pubplaceParís
local.citation.publicationNameIFAC-PapersOnLine (volume 48, issue 21, Pages 1-1496): 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 Paris, 2–4 September 2015
local.citation.startingPage434
local.citation.endingPage439


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