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dc.contributor.authorSaucedo Dorantes, Juan Jose
dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.authorRomero Troncoso, René
dc.contributor.authorOsornio Rios, Roque A.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2019-07-26T07:34:18Z
dc.date.available2019-07-26T07:34:18Z
dc.date.issued2019-08-01
dc.identifier.citationSaucedo, J. [et al.]. Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine. "Applied soft computing", 1 Agost 2019, vol. 81, núm. 105497, p. 1-12.
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/2117/166892
dc.description.abstractStrategies of condition monitoring applied to electric motors play an important role in the competitiveness of multiple industrial sectors. However, the risk of faults coexistence in an electric motor and the overlapping of their effects in the considered physical magnitudes represent, currently, a critical limitation to provide reliable diagnosis outcomes. In this regard, additional investigation efforts are required towards high-dimensional data fusion schemes, particularly over the features calculation and features reduction, which represent two decisive stages in such data-driven approaches. In this study, a novel multiple-fault detection and identification methodology supported by a feature-level fusion strategy and a Self-Organizing Maps (SOM) hierarchical structure is proposed. The condition diagnosis as well as the corresponding estimated probability are obtained. Moreover, the proposed method allows the visualization of the results while preserving the underlying physical phenomenon of the system under monitoring. The proposed scheme is performed sequentially; first, a set of statistical-time based features is estimated from different physical magnitudes. Second, a hybrid feature reduction method is proposed, composed by an initial soft feature reduction, based on sequential floating forward selection to remove the less informative features, and followed by a hierarchical SOM structure which reveals directly the diagnosis and probability assessment. The effectiveness of the proposed detection and identification scheme is validated with a complete set of experimental data including healthy and five faulty conditions. The accuracy’s results are compared with classical condition monitoring approaches in order to validate the competency of the proposed method.
dc.format.extent12 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria
dc.subject.lcshApplication software
dc.subject.lcshVibration
dc.subject.lcshTemperature
dc.subject.lcshTime-series analysi
dc.subject.lcshElectric motors, Induction
dc.subject.lcshDiagnostic errors
dc.subject.otherCondition monitoring
dc.subject.otherFault diagnosis
dc.subject.otherFeature estimation
dc.subject.otherFeature reduction
dc.subject.otherInduction motor
dc.subject.otherSelf-organizing feature maps
dc.subject.otherTime series analysis
dc.subject.otherSequential selection
dc.subject.otherStator currents
dc.subject.otherTemperatures
dc.subject.otherVibrations
dc.titleMultiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine
dc.typeArticle
dc.subject.lemacProgramari d'aplicació
dc.subject.lemacVibració
dc.subject.lemacTemperatura
dc.subject.lemacSèries temporals--Anàlisi
dc.subject.lemacMotors elèctrics d'inducció
dc.subject.lemacErrors de diagnòstic
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1016/j.asoc.2019.105497
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1568494619302674
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac25177195
dc.description.versionPostprint (author's final draft)
dc.date.lift2021-08-01
local.citation.authorSaucedo, J.; Delgado Prieto, M.; Romero, R.; Osornio, R.
local.citation.publicationNameApplied soft computing
local.citation.volume81
local.citation.number105497
local.citation.startingPage1
local.citation.endingPage12


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