Sakallı, G.Koyuncu, H.2023-05-302023-05-3020229.78E+12https://doi.org/10.1109/ICDABI56818.2022.10041492https://hdl.handle.net/20.500.13091/40582022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October 2022 through 26 October 2022 -- 186761Asynchronous 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.eninfo:eu-repo/semantics/closedAccessclassificationdeep learningimage analysisinfrared imagemotor faulttransfer learningDeep learningImage analysisImage classificationInfrared devicesInfrared imagingTemperature indicating camerasDeep learningImage analyzeImage-analysisInductions motorsInfra-red camerasInfrared camerasInfrared imageMotor faultTest methodTransfer learningInduction motorsConference Object10.1109/ICDABI56818.2022.100414922-s2.0-85149299832