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dc.contributor.authorArellano Espitia, Francisco
dc.contributor.authorGonzález Abreu, Artvin Darién
dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.authorSaucedo Dorantes, Juan Jose
dc.contributor.authorOsornio Rios, Roque A.
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Electrònica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2020-10-15T12:59:22Z
dc.date.issued2020
dc.identifier.citationArellano, F. [et al.]. Analysis of machine learning based condition monitoring schemes applied to complex electromechanical systems. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1419-1422. ISBN 978-1-7281-8957-4. DOI 10.1109/ETFA46521.2020.9212026.
dc.identifier.isbn978-1-7281-8957-4
dc.identifier.urihttp://hdl.handle.net/2117/330308
dc.description.abstractIn the modern industry framework, the application of condition monitoring schemes over electromechanical systems is being subjected to demanding requirements. Currently, the massive digitalization of industrial assets allows the investigation towards multiple monitoring strategies capable of emphasize deviations over the nominal system operation. However, the most prominent techniques, such as Machine Learning, present great challenges in complex systems. In this regard, the proposed study presents the analysis of the diagnostic capabilities resulting from the classical approaches based on machine learning facing to complex electromechanical systems that implies a working environment subject to different operation condition, configurations with multiple components and the presence of faults of different nature (mechanical, electrical, electromagnetic), under isolated or combined scenarios. Discriminative feature extraction capabilities and classification accuracy will be analyzed as performance measures.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.otherCondition monitoring
dc.subject.otherFault detection
dc.subject.otherMachine learning
dc.subject.otherDeep learning
dc.titleAnalysis of machine learning based condition monitoring schemes applied to complex electromechanical systems
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/ETFA46521.2020.9212026
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/9212026
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac29516693
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorArellano, F.; González, A.D.; Delgado Prieto, M.; Saucedo, J.; Osornio, R.
local.citation.contributorIEEE International Conference on Emerging Technologies and Factory Automation
local.citation.publicationName2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020
local.citation.startingPage1419
local.citation.endingPage1422


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