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|Title:||A Comparison of Deep Transfer Learning Methods on Bearing Fault Detection||Authors:||Deveci, B.U.
bearing fault diagnostics
CWRU bearing dataset
Bearings (machine parts)
Bearing fault diagnostics
Case Western Reserve University
Case western reserve university bearing dataset
|Issue Date:||2021||Publisher:||Institute of Electrical and Electronics Engineers Inc.||Abstract:||In rotating machinery, bearings are widely used as universal components. Bearings are placed in critical positions, therefore, in predictive maintenance, it is crucial to diagnose bearing faults accurately and in a timely manner. In this paper, three diverse pre-trained networks on bearing fault diagnosis are discussed. A generic intelligent bearing fault diagnosis system based on AlexNet, GoogLeNet and ResNet-50 with transfer learning is proposed to distinguish and classify different bearing faults. Three bearing faults at all various loads and speeds selected from the Case Western Reserve University (CWRU) bearing dataset were converted to time-frequency images, in order to improve the performance of the proposed networks. Results showed that when compared to previous methods, the proposed method achieved outstanding execution, with overall classification training accuracy of 100%, validation accuracy of 99.27%. © 2021 IEEE.||Description:||8th International Conference on Future Internet of Things and Cloud, FiCloud 2021 -- 23 August 2021 through 25 August 2021 -- 173916||URI:||https://doi.org/10.1109/FiCloud49777.2021.00048
|Appears in Collections:||Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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checked on Nov 27, 2023
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