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dc.contributor.authorSaucedo Dorantes, Juan Jose
dc.contributor.authorOsornio Rios, Roque A.
dc.contributor.authorRomero Troncoso, René
dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.authorArellano Espitia, Francisco
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Electrònica
dc.date.accessioned2019-11-21T15:12:40Z
dc.date.issued2019
dc.identifier.citationSaucedo, J. [et al.]. Novel condition monitoring approach based on hybrid feature extraction and neural network for assessing multiple faults in electromechanical systems. A: IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. "SDEMPED - 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED): 27-30 August 2019: Toulouse, France". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 466-473.
dc.identifier.isbn978-1-7281-1832-1
dc.identifier.urihttp://hdl.handle.net/2117/172842
dc.description.abstractNew challenges involve the development of new condition monitoring approaches to avoid unexpected downtimes and to ensure the availability of machines during operating working conditions. The feature calculation from vibrations and stator currents is one of the most common an important signal processing included in condition monitoring strategies; however, the calculation of features from only one signal alone can only detect some specific faults. Thus, disadvantages are presented if multiple faults are addressed. Aiming to avoid this issue, in this work is proposed a novel condition monitoring approach based on a hybrid feature calculation of statistical features from the available vibrations and stator current signals. Thus, the characterization of the available signals is performed by estimating a hybrid set of features, then, through the Linear Discriminant Analysis, such hybrid set of features is subjected to a dimensionality reduction procedure resulting into a 2-dimensional space. Finally, the assessment and identification of multiple faulty conditions are carried out through a Neural Network. The effectiveness of the proposed approach is validated by its application to two different experimental test benches, which makes the proposed approach feasible to be applied in industrial processes.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria elèctrica::Electromecànica
dc.subject.lcshElectromechanical devices
dc.subject.otherCondition monitoring
dc.subject.otherElectromechanical systems
dc.subject.otherFeature extraction
dc.subject.otherTime-domain analysis
dc.subject.otherFrequency-domain analysis
dc.subject.otherLinear discriminant analysis
dc.subject.otherVibrations
dc.subject.otherCurrent measurement
dc.titleNovel condition monitoring approach based on hybrid feature extraction and neural network for assessing multiple faults in electromechanical systems
dc.typeConference report
dc.subject.lemacDispositius electromecànics
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/DEMPED.2019.8864835
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/8864835
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac25970012
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorSaucedo, J.; Osornio, R.; Romero, R.; Delgado Prieto, M.; Arellano, F.
local.citation.contributorIEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives
local.citation.publicationNameSDEMPED - 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED): 27-30 August 2019: Toulouse, France
local.citation.startingPage466
local.citation.endingPage473


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