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Bearing Fault Detection and Diagnosis Based on Densely Connected Convolutional Networks Cover

Bearing Fault Detection and Diagnosis Based on Densely Connected Convolutional Networks

Acta Mechanica et Automatica
Volume 16 (2022): Issue 2 (June 2022)
By: Julius Niyongabo,  Yingjie Zhang and  Jérémie Ndikumagenge  
Open Access
|Mar 2022

Authors

Julius Niyongabo

niyojule@gmail.com

Doctoral School, University of Burundi, Burundi

Yingjie Zhang

zhangyj@hnu.edu.cn

College of Computer Science and Electronic Engineering, Hunan University, China

Jérémie Ndikumagenge

jeremie.ndikumagenge@ub.edu.bi

Faculty of Engineering Science, Department of Information and Communication Technology, University of Burundi, Burundi
DOI: https://doi.org/10.2478/ama-2022-0017 | Journal eISSN: 2300-5319 | Journal ISSN: 1898-4088
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Language: English
Page range: 130 - 135
Submitted on: Dec 18, 2021
Accepted on: Feb 12, 2022
Published on: Mar 24, 2022
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
bearing,
deep learning,
machine learning,
transfer learning,
fault detection and diagnosis,
CWRU dataset
Related subjects:
Engineering,
Electrical engineering,
Electronics,
Mechanical engineering,
Mechanics,
Bioengineering and biomedical engineering,
Biomechanics,
Civil engineering,
Environmental engineering

© 2022 Julius Niyongabo, Yingjie Zhang, Jérémie Ndikumagenge, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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