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dc.contributor.authorSaucedo Dorantes, Juan Jose
dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.authorOsornio Rios, Roque A.
dc.contributor.authorRomero Troncoso, Rene De Jesus
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2018-04-24T12:31:28Z
dc.date.available2018-04-24T12:31:28Z
dc.date.issued2016-12-07
dc.identifier.citationSaucedo, J., Delgado Prieto, M., Osornio, R., Romero-Troncoso, R.J. Multifault diagnosis method applied to an electric machine based on high-dimensional feature reduction. "IEEE transactions on industry applications", 7 Desembre 2016, vol. 53, núm. 3, p. 3086-3097.
dc.identifier.issn0093-9994
dc.identifier.urihttp://hdl.handle.net/2117/116623
dc.description.abstractCondition monitoring schemes are essential for increasing the reliability and ensuring the equipment efficiency in industrial processes. The feature extraction and dimensionality reduction are useful preprocessing steps to obtain high performance in condition monitoring schemes. To address this issue, this work presents a novel diagnosis methodology based on high-dimensional feature reduction applied to detect multiple faults in an induction motor linked to a kinematic chain. The proposed methodology involves a hybrid feature reduction that ensures a good processing of the acquired vibration signals. The method is performed sequentially. First, signal decomposition is carried out by means of empirical mode decomposition. Second, statistical-time-based features are estimated from the resulting decompositions. Third, a feature optimization is performed to preserve the data variance by a genetic algorithm in conjunction with the principal component analysis. Fourth, a feature selection is done by means of Fisher score analysis. Fifth, a feature extraction is performed through linear discriminant analysis. And, finally, sixth, the different considered faults are diagnosed by a Neural Network-based classifier. The performance and the effectiveness of the proposed diagnosis methodology is validated experimentally and compared with classical feature reduction strategies, making the proposed methodology suitable for industry applications.
dc.format.extent12 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria mecànica::Motors
dc.subjectÀrees temàtiques de la UPC::Enginyeria dels materials
dc.subject.lcshElectric motors, Induction
dc.subject.lcshMaintenance
dc.subject.otherCondition monitoring
dc.subject.otherfeature reduction
dc.subject.otherinduction motor (IM)
dc.subject.othermultiple faults
dc.subject.othervibrations
dc.titleMultifault diagnosis method applied to an electric machine based on high-dimensional feature reduction
dc.typeArticle
dc.subject.lemacMotors elèctrics d'inducció
dc.subject.lemacMotors elèctrics
dc.subject.lemacMaquinària -- Manteniment i reparació
dc.subject.lemacManteniment
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/TIA.2016.2637307
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7776827/
dc.rights.accessOpen Access
local.identifier.drac21103215
dc.description.versionPostprint (author's final draft)
local.citation.authorSaucedo, J.; Delgado Prieto, M.; Osornio, R.; Romero-Troncoso, R.J.
local.citation.publicationNameIEEE transactions on industry applications
local.citation.volume53
local.citation.number3
local.citation.startingPage3086
local.citation.endingPage3097


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