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DC Field | Value | Language |
---|---|---|
dc.creator | Borges, Fernando | - |
dc.creator | Pinto, Andrey | - |
dc.creator | Ribeiro, Diogo | - |
dc.creator | Barbosa, Tássio | - |
dc.creator | Pereira, Daniel | - |
dc.creator | Magalhães, Ricardo | - |
dc.creator | Barbosa, Bruno | - |
dc.creator | Ferreira, Danton | - |
dc.date.accessioned | 2021-06-02T18:12:23Z | - |
dc.date.available | 2021-06-02T18:12:23Z | - |
dc.date.issued | 2020-05 | - |
dc.identifier.citation | BORGES, 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.uri | https://ieeexplore.ieee.org/document/9099687 | pt_BR |
dc.identifier.uri | http://repositorio.ufla.br/jspui/handle/1/46447 | - |
dc.description.abstract | In 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.language | en_US | pt_BR |
dc.publisher | Institute of Electrical and Electronic Engineers - IEEE | pt_BR |
dc.rights | restrictAccess | pt_BR |
dc.source | IEEE Latin America Transactions (IEEE LATAM) | pt_BR |
dc.subject | Support vector machines | pt_BR |
dc.subject | Fault detection | pt_BR |
dc.subject | Feature extraction | pt_BR |
dc.subject | Higher order statistics | pt_BR |
dc.subject | Monitoring | pt_BR |
dc.subject | Máquina de vetores de suporte | pt_BR |
dc.subject | Falhas mecânicas - Detecção | pt_BR |
dc.subject | Estatísticas de ordem superior | pt_BR |
dc.title | An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection | pt_BR |
dc.type | Artigo | pt_BR |
Appears in Collections: | DAT - Artigos publicados em periódicos DEG - Artigos publicados em periódicos |
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