Transfer learning with inception ResNet-based model for rolling bearing fault diagnosis
Author:
Affiliation:
1. Department of Power Engineering, Naval University of Engineering
Publisher
Japan Society of Mechanical Engineers
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://www.jstage.jst.go.jp/article/jamdsm/16/2/16_2022jamdsm0023/_pdf
Reference46 articles.
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3. Cerrada, Mariela, et al., A review on data-driven fault severity assessment in rolling bearings, Mechanical Systems and Signal Processing, Vol. 99 (2018), pp. 169-196.
4. Cheng, Yiwei, et al., Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network, Knowledge-Based Systems, Vol. 216 (2021), pp. 106796.
5. Choudhary A, Mian T and Fatima S, Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images, Measurement, Vol. 176 (2021), pp. 109196.
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