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dc.creatorBorges, Fernando-
dc.creatorPinto, Andrey-
dc.creatorRibeiro, Diogo-
dc.creatorBarbosa, Tássio-
dc.creatorPereira, Daniel-
dc.creatorMagalhães, Ricardo-
dc.creatorBarbosa, Bruno-
dc.creatorFerreira, Danton-
dc.date.accessioned2021-06-02T18:12:23Z-
dc.date.available2021-06-02T18:12:23Z-
dc.date.issued2020-05-
dc.identifier.citationBORGES, F. et al. An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection. IEEE Latin America Transactions, [S. I.], v. 18, n. 06, p. 1093-1101, Jun. 2020. DOI: 10.1109/TLA.2020.9099687.pt_BR
dc.identifier.urihttps://ieeexplore.ieee.org/document/9099687pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/46447-
dc.description.abstractIn this paper an unsupervised method to detect mechanical faults using support vector machines and higher-order statistics is proposed. The method extracts compact vector features - based on higher-order statistics - from vibration signals and uses the one-class support vector machine to build a closed region around the data from the health structure. The method was evaluated considering two cases: fault detection in a cantilever beam and in a three-phase induction motor. In both cases, the vibrations were collected by a 3 axis accelerometer sensor. The acquisition system was controlled by an open-source electronic prototyping ARDUINO ® platform. After collecting the data, higher-order statistics-based features were extracted. These features were presented to the one-class support vector machine for fault detection. The proposed method was capable of identifying a closed region in a two-dimensional space so that events inside this region are signed as no faults and events outside this region are signed as faults. The method has two important characteristics: (i) it requires only healthy mechanical structures to be designed, and (ii) it operates in a low dimensional space (only two) constructed by the higher-order statistics features, which requires low computational cost in the operational phase.pt_BR
dc.languageen_USpt_BR
dc.publisherInstitute of Electrical and Electronic Engineers - IEEEpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceIEEE Latin America Transactions (IEEE LATAM)pt_BR
dc.subjectSupport vector machinespt_BR
dc.subjectFault detectionpt_BR
dc.subjectFeature extractionpt_BR
dc.subjectHigher order statisticspt_BR
dc.subjectMonitoringpt_BR
dc.subjectMáquina de vetores de suportept_BR
dc.subjectFalhas mecânicas - Detecçãopt_BR
dc.subjectEstatísticas de ordem superiorpt_BR
dc.titleAn Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detectionpt_BR
dc.typeArtigopt_BR
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