A novel feature extraction method for bearing fault classification with one dimensional ternary patterns

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dc.authoridKaplan, Kaplan/0000-0001-8036-1145
dc.authoridKUNCAN, Melih/0000-0002-9749-0418
dc.contributor.authorKuncan, Melih
dc.contributor.authorKaplan, Kaplan
dc.contributor.authorMinaz, Mehmet Recep
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorErtunc, H. Metin
dc.date.accessioned2024-12-24T19:27:20Z
dc.date.available2024-12-24T19:27:20Z
dc.date.issued2020
dc.departmentSiirt Üniversitesi
dc.description.abstractBearing is one of the most critical parts used in rotary machines. Bearing faults break down the mechanism where it is located. Moreover, the faults may cause to malfunction by spreading to the entire system. Thus this may result in catastrophic failure eventually. Precise and decisive feature extraction from the raw vibration signal maintains to be one of the current topics explored for fault diagnosis in bearings. In this study, vibration signals are obtained from bearings which are formed with artificial faults of specific dimensions from a bearing test setup. Instead of employing traditional feature extraction methods found in the literature, a novel feature extraction method for bearing faults called one-dimensional ternary pattern (1D-TP) is applied. The proposed approach is a statistical method that uses patterns obtained from comparisons between neighbors of each value on vibration signals. The study aims to identify the size (mm) of the fault by determining the bearing part (inner ring, outer ring, ball) from which the faults in the bearings are caused. Several classification techniques were performed by using ternary patterns with RF (Random Forest), k-NN (1<-nearest neighbor), SVM (Support Vector Machine), BayesNet, ANN (Artificial Neural Networks) models. As a result of analyzing the signals obtained from the experimental setup with the proposed model, 91.25% for dataset_1 (different speed), 100% for dataset_2 (fault type - inner ring, outer ring, ball) and 100% for dataset_3 (fault size (mm)) success rates are determined. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.isatra.2019.11.006
dc.identifier.endpage357
dc.identifier.issn0019-0578
dc.identifier.issn1879-2022
dc.identifier.pmid31732141
dc.identifier.scopus2-s2.0-85075380253
dc.identifier.scopusqualityQ1
dc.identifier.startpage346
dc.identifier.urihttps://doi.org/10.1016/j.isatra.2019.11.006
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6606
dc.identifier.volume100
dc.identifier.wosWOS:000532697600031
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Science Inc
dc.relation.ispartofIsa Transactions
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectFeature extraction
dc.subject1D-TP
dc.subjectFault classification
dc.subjectDiagnosis
dc.subjectArtificial intelligence
dc.subjectBearing fault in servo-motor
dc.titleA novel feature extraction method for bearing fault classification with one dimensional ternary patterns
dc.typeArticle

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