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dc.contributor.authorZurita Millán, Daniel
dc.contributor.authorCariño Corrales, Jesús Adolfo
dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.authorOrtega Redondo, Juan Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2015-03-24T13:52:26Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationZurita, D. [et al.]. Distributed neuro-fuzzy feature forecasting approach for condition monitoring. A: IEEE International Conference on Emerging Technologies and Factory Automation. "Proceedings of the 19th IEEE International Conference on Emerging Technologies and Factory Automation". Barcelona: Institute of Electrical and Electronics Engineers (IEEE), 2014.
dc.identifier.isbn978-1-4799-4846-8
dc.identifier.urihttp://hdl.handle.net/2117/26982
dc.description.abstractThe industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach is proposed, in which not only the current status of the system under monitoring in identified, diagnosis, but also the future condition is assessed, prognosis. The novelties of the proposed methodology are based on a distributed features forecasting approach by means of adaptive neuro-fuzzy inference system models. The proposed method is validated by means of an accelerated bearing degradation experimental platform.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshArtificial intelligence
dc.subject.other{condition monitoring
dc.subject.otherfeature selection
dc.subject.otherfuzzy neural nets
dc.subject.otherfuzzy reasoning
dc.subject.otherpattern classification
dc.subject.otherreliability
dc.subject.otherANFIS
dc.subject.otheradaptive neuro-fuzzy inference system models
dc.subject.othercondition monitoring capabilities
dc.subject.otherdistributed neuro-fuzzy feature forecasting approach
dc.subject.otherfeature calculation
dc.subject.otherfeature classification
dc.subject.otherfeature reduction
dc.subject.otherindustrial machinery reliability
dc.subject.otherArtificial neural networks
dc.subject.otherDegradation
dc.subject.otherForecasting
dc.subject.otherPredictive models
dc.subject.otherPrognostics and health management
dc.subject.otherTime-domain analysis
dc.subject.otherTraining
dc.subject.otherArtificial intelligence
dc.subject.otherCondition monitoring
dc.subject.otherFeature extraction
dc.subject.otherFuzzy neural networks
dc.subject.otherMachine learning
dc.subject.otherPrognosis
dc.subject.otherRemaining Useful Life
dc.subject.otherTime domain analysis}
dc.titleDistributed neuro-fuzzy feature forecasting approach for condition monitoring
dc.typeConference report
dc.subject.lemacIntel·ligència artificial
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/ETFA.2014.7005180
dc.relation.publisherversionhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7005180&isnumber=7005023
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac15535896
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorZurita, D.; Cariño , J.A.; Delgado, M.; Ortega, J.
local.citation.contributorIEEE International Conference on Emerging Technologies and Factory Automation
local.citation.pubplaceBarcelona
local.citation.publicationNameProceedings of the 19th IEEE International Conference on Emerging Technologies and Factory Automation


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