Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters

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dc.authoridKUNCAN, Melih/0000-0002-9749-0418
dc.authoridKaplan, Kaplan/0000-0001-8036-1145
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorKuncan, Melih
dc.contributor.authorKaplan, Kaplan
dc.contributor.authorMinaz, Mehmet Recep
dc.contributor.authorErtunc, H. Metin
dc.date.accessioned2024-12-24T19:24:26Z
dc.date.available2024-12-24T19:24:26Z
dc.date.issued2020
dc.departmentSiirt Üniversitesi
dc.description.abstractBearings are the most commonly used machine element in order to reduce rotational friction in machines and to compensate radial and axial loads. It is very important to determine the faults in the bearings in terms of the machine health. In order to accurately diagnose bearing-related faults with traditional machine learning methods, it is necessary to identify the features that characterize bearing fault most accurately. Therefore, a new feature extraction procedure has been proposed to determine the vibration signal velocities of different fault sizes and types in this study. The new approach has been employed to obtain features from the vibration signals for different scenarios. After different filtering based on 1D-LBP method, the F-1D-LBP method was used to construct feature vectors. The filters reduce the noise in the signals and provide different feature groups. In other words, it is aimed to generate filters in order to extract different patterns that can separate signals. For each filter applied, different patterns can be obtained for the same local point on signals. Thus, the signals can be represented by different feature vectors. Then, by using these feature groups with various machine learning methods, vibration velocities were separated from each other. As a result, it was observed that the obtained feature had promising results for classification of bearing vibrations.
dc.identifier.doi10.1007/s00500-019-04656-2
dc.identifier.endpage12186
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85077527234
dc.identifier.scopusqualityQ1
dc.identifier.startpage12175
dc.identifier.urihttps://doi.org/10.1007/s00500-019-04656-2
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5989
dc.identifier.volume24
dc.identifier.wosWOS:000505421400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofSoft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectF-1D-LBP
dc.subjectFeature extraction
dc.subjectBearing fault diagnosis
dc.subjectMachine learning
dc.titleClassification of bearing vibration speeds under 1D-LBP based on eight local directional filters
dc.typeArticle

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