An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorBorges, Fernando-
Autor(es): dc.creatorPinto, Andrey-
Autor(es): dc.creatorRibeiro, Diogo-
Autor(es): dc.creatorBarbosa, Tássio-
Autor(es): dc.creatorPereira, Daniel-
Autor(es): dc.creatorMagalhães, Ricardo-
Autor(es): dc.creatorBarbosa, Bruno-
Autor(es): dc.creatorFerreira, Danton-
Data de aceite: dc.date.accessioned2026-02-09T12:00:11Z-
Data de disponibilização: dc.date.available2026-02-09T12:00:11Z-
Data de envio: dc.date.issued2021-06-02-
Data de envio: dc.date.issued2021-06-02-
Data de envio: dc.date.issued2020-05-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/46447-
Fonte completa do material: dc.identifierhttps://ieeexplore.ieee.org/document/9099687-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1152207-
Descrição: dc.descriptionIn 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.-
Idioma: dc.languageen-
Publicador: dc.publisherInstitute of Electrical and Electronic Engineers - IEEE-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceIEEE Latin America Transactions (IEEE LATAM)-
Palavras-chave: dc.subjectSupport vector machines-
Palavras-chave: dc.subjectFault detection-
Palavras-chave: dc.subjectFeature extraction-
Palavras-chave: dc.subjectHigher order statistics-
Palavras-chave: dc.subjectMonitoring-
Palavras-chave: dc.subjectMáquina de vetores de suporte-
Palavras-chave: dc.subjectFalhas mecânicas - Detecção-
Palavras-chave: dc.subjectEstatísticas de ordem superior-
Título: dc.titleAn Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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