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dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.authorCariño Corrales, Jesús Adolfo
dc.contributor.authorSaucedo Dorantes, Juan Jose
dc.contributor.authorOsornio Rios, Roque A.
dc.contributor.authorRomeral Martínez, José Luis
dc.contributor.authorRomero Troncoso, René
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2019-01-15T13:59:22Z
dc.date.available2019-01-15T13:59:22Z
dc.date.issued2018
dc.identifier.citationDelgado Prieto, M., Cariño, J. A., Saucedo, J., Osornio, R., Romeral, L., Romero, R. Novelty detection based condition monitoring scheme applied to electromechanical systems. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA): Politecnico di Torino, Torino, Italy, 04-07 September, 2018: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1213-1216.
dc.identifier.isbn978-1-5386-7107-8
dc.identifier.otherhttps://ieeexplore.ieee.org/document/8502503
dc.identifier.urihttp://hdl.handle.net/2117/126832
dc.description.abstractThis study is focused on the current challenges dealing with electromechanical system monitoring applied in industrial frameworks, that is, the presence of unknown events and the limitation to the nominal healthy condition as starting knowledge. Thus, an industrial machinery condition monitoring methodology based on novelty detection and classification is proposed in this study. The methodology is divided in three main stages. First, a dedicated feature calculation and reduction over each available physical magnitude. Second, an ensemble structure of novelty detection models based on one-class support vector machines to identify not previously considered events. Third, a diagnosis model supported by a feature fusion scheme in order to reach high fault classification capabilities. The effectiveness of the fault detection and identification methodology has been compared with classical single model approach, and verified by experimental results obtained from an electromechanical machine. © 2018 IEEE.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Energies::Energia elèctrica::Automatització i control de l'energia elèctrica
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subject.lcshAutomation
dc.subject.lcshElectric power systems
dc.subject.lcshVibration
dc.subject.othercondition monitoring
dc.subject.otherfault diagnosis
dc.subject.otherindustry applications
dc.subject.othermachine learning
dc.subject.othercomputer aided diagnosis
dc.subject.othercondition monitoring
dc.subject.otherfactory automation
dc.subject.otherfailure analysis
dc.subject.otherlearning algorithms
dc.subject.otherlearning systems
dc.subject.othermachinery
dc.subject.otherelectromechanical systems
dc.subject.otherensemble structures
dc.subject.otherfault classification
dc.subject.otherfault detection and identification
dc.subject.otherindustrial machinery
dc.subject.otherindustry applications
dc.subject.otherone-class support vector machine
dc.subject.othersingle model approach
dc.subject.otherfault detection
dc.titleNovelty detection based condition monitoring scheme applied to electromechanical systems
dc.typeConference report
dc.subject.lemacAutomatització
dc.subject.lemacSistemes de distribució d'energia elèctrica
dc.subject.lemacVibració
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/ETFA.2018.8502503
dc.rights.accessOpen Access
local.identifier.drac23519199
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TRA2016-80472-R
local.citation.authorDelgado Prieto, M.; Cariño, J. A.; Saucedo, J.; Osornio, R.; Romeral, L.; Romero, R.
local.citation.contributorIEEE International Conference on Emerging Technologies and Factory Automation
local.citation.publicationName2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA): Politecnico di Torino, Torino, Italy, 04-07 September, 2018: proceedings
local.citation.startingPage1213
local.citation.endingPage1216


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