Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4058
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sakallı, G. | - |
dc.contributor.author | Koyuncu, H. | - |
dc.date.accessioned | 2023-05-30T21:09:03Z | - |
dc.date.available | 2023-05-30T21:09:03Z | - |
dc.date.issued | 2022 | - |
dc.identifier.isbn | 9.78167E+12 | - |
dc.identifier.uri | https://doi.org/10.1109/ICDABI56818.2022.10041492 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/4058 | - |
dc.description | 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October 2022 through 26 October 2022 -- 186761 | en_US |
dc.description.abstract | Asynchronous or induction motors are frequently preferred in industrial applications concerning their cheap supply, strength and easy maintenance. However, the fault recognition of these motors constitutes a comprehensive examination with direct interference. As a consequence, the images obtained from infrared cameras and their analyses gain importance to remotely detect the situation of motors. For this purpose, we handle a transfer learning approach named ResNet50 to categorize 11 different situations of asynchronous motors in infrared camera images. For performance assessment, hyper-parameters of ResNet50 are examined to maximize the success to be achieved. In experiments, two test methods (70%-30% training-test split and 80%-20% training-test split) are utilized to objectively evaluate the parameter adjustments and to obviously reveal the effect of training samples. As a result, it's proven that ResNet50 can achieve 100% classification accuracy for categorization of induction motor situations in experiments with both test methods. © 2022 IEEE. | en_US |
dc.description.sponsorship | ACKNOWLEDGMENT This work is supported by the Coordinatorship Technical University's Scientific Research Projects. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | classification | en_US |
dc.subject | deep learning | en_US |
dc.subject | image analysis | en_US |
dc.subject | infrared image | en_US |
dc.subject | motor fault | en_US |
dc.subject | transfer learning | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Image analysis | en_US |
dc.subject | Image classification | en_US |
dc.subject | Infrared devices | en_US |
dc.subject | Infrared imaging | en_US |
dc.subject | Temperature indicating cameras | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Image analyze | en_US |
dc.subject | Image-analysis | en_US |
dc.subject | Inductions motors | en_US |
dc.subject | Infra-red cameras | en_US |
dc.subject | Infrared cameras | en_US |
dc.subject | Infrared image | en_US |
dc.subject | Motor fault | en_US |
dc.subject | Test method | en_US |
dc.subject | Transfer learning | en_US |
dc.subject | Induction motors | en_US |
dc.title | Categorization of Asynchronous Motor Situations in Infrared Images: Analyses with ResNet50 | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/ICDABI56818.2022.10041492 | - |
dc.identifier.scopus | 2-s2.0-85149299832 | en_US |
dc.department | KTÜN | en_US |
dc.identifier.startpage | 114 | en_US |
dc.identifier.endpage | 118 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57796435700 | - |
dc.authorscopusid | 55884277600 | - |
dc.ktun-update | ktunupdate | en_US |
item.fulltext | No Fulltext | - |
item.openairetype | Conference Object | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
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