A new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods

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dc.contributor.authorKaya, Yilmaz
dc.contributor.authorKuncan, Fatma
dc.contributor.authorErtunc, H. Metin
dc.date.accessioned2024-12-24T19:32:57Z
dc.date.available2024-12-24T19:32:57Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractBearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today's machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time savings and high success rates. Deep transfer learning models also improve diagnosis accuracy under certain conditions by greatly reducing human intervention. Diagnosis at the right time is very important for the sustainability and efficiency of industrial production. A technique based on continuous wavelet transform (CWT) and two dimensional (2D) convolutional neural networks (CNN) is presented in this paper to detect fault size from vibration data of various bearing failure types. Time-frequency (TF) color scalogram images for bearing vibration signals were obtained using the CWT method. Using AlexNet, GoogleNet, Resnet, VGG16, and VGG19 deep transfer learning methods with scalogram images, fault size prediction from vibration signals was performed. Five different transfer deep learning models were used for three different data sets. It was observed that the success rates obtained varied between 96.67% and 100%.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Siirt University [2021-SIUMUH-14]
dc.description.sponsorshipThis work was supported by the Scientific Research Projects Coordination Unit of Siirt University as a project with the number 2021-SIUMUH-14. This study was performed in Siirt University Faculty of Engineering Machine Vision (MaVi) Laboratory. The authors would like to thank the staff of MaVi Laboratory for their support.
dc.identifier.doi10.55730/1300-0632.3909
dc.identifier.endpage1867
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85138620274
dc.identifier.scopusqualityQ2
dc.identifier.startpage1851
dc.identifier.trdizinid1142452
dc.identifier.urihttps://doi.org/10.55730/1300-0632.3909
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1142452
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7896
dc.identifier.volume30
dc.identifier.wosWOS:000904725600012
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectBearing faults
dc.subjectfault detection
dc.subjectfault classification
dc.subjectcontinuous wavelet transform
dc.subjectconvolutional neural
dc.subjectnetworks
dc.subjecttransfer learning
dc.titleA new automatic bearing fault size diagnosis using time-frequency images of CWT and deep transfer learning methods
dc.typeArticle

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