A deep ensemble dense convolutional neural network for rolling bearing fault diagnosis
Author:
Funder
National Natural Science Foundation of China
Publisher
IOP Publishing
Subject
Applied Mathematics,Instrumentation,Engineering (miscellaneous)
Link
https://iopscience.iop.org/article/10.1088/1361-6501/ac05f5/pdf
Reference38 articles.
1. Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis;Xu;Meas. Sci. Technol.,2019
2. A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification;Yilmaz;J. Exp. Theor. Artif. Intell.,2020
3. Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters;Yilmaz;Soft Comput.,2020
4. An intelligent approach for bearing fault diagnosis: combination of 1D-LBP and GRA;Melih;IEEE Access,2020
5. An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis;Kaplan;Appl. Soft Comput.,2020
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