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Title: Categorization of Asynchronous Motor Situations in Infrared Images: Analyses with ResNet50
Authors: Sakallı, G.
Koyuncu, H.
Keywords: classification
deep learning
image analysis
infrared image
motor fault
transfer learning
Deep learning
Image analysis
Image classification
Infrared devices
Infrared imaging
Temperature indicating cameras
Deep learning
Image analyze
Inductions motors
Infra-red cameras
Infrared cameras
Infrared image
Motor fault
Test method
Transfer learning
Induction motors
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
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.
Description: 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October 2022 through 26 October 2022 -- 186761
ISBN: 9.78167E+12
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

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