A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification

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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.accessioned2024-12-24T19:28:11Z
dc.date.available2024-12-24T19:28:11Z
dc.date.issued2021
dc.departmentSiirt Üniversitesi
dc.description.abstractRecently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration signals was proposed instead of the conventional feature extraction methods, as in the literature. The One (1) Dimensional-Local Binary Patterns (1D-LBP) method was first applied to bearing vibration signals, and a new signal whose values ranged between 0-255 was obtained. Then, co-occurrence matrices were obtained from these signals. The correlation, energy, homogeneity, and contrast features were extracted from these matrices. Different machine learning methods were employed with these features to carry out the classification process. Three different data sets were used to test the proposed approach. As a result of analysing the signals with the proposed model, the success rate is 87.50% for dataset1 (different speed), 96.5% for dataset2 (fault size (mm)) and 99.30% for dataset3 (fault type - inner ring, outer ring, ball) was found, respectively.
dc.identifier.doi10.1080/0952813X.2020.1735530
dc.identifier.endpage178
dc.identifier.issn0952-813X
dc.identifier.issn1362-3079
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85081328739
dc.identifier.scopusqualityQ1
dc.identifier.startpage161
dc.identifier.urihttps://doi.org/10.1080/0952813X.2020.1735530
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6950
dc.identifier.volume33
dc.identifier.wosWOS:000518747500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofJournal of Experimental & Theoretical Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectFeature extraction
dc.subject1d-LBP
dc.subject1d-GLCM
dc.subjectfault classification
dc.subjectbearing fault diagnosis
dc.titleA new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification
dc.typeArticle

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