Version 1
: Received: 12 September 2024 / Approved: 13 September 2024 / Online: 13 September 2024 (07:11:03 CEST)
How to cite:
Xu, M. Research on Bearing Fault Diagnosis Methods Based on Various Convolutional Neural Network Architectures. Preprints2024, 2024091055. https://doi.org/10.20944/preprints202409.1055.v1
Xu, M. Research on Bearing Fault Diagnosis Methods Based on Various Convolutional Neural Network Architectures. Preprints 2024, 2024091055. https://doi.org/10.20944/preprints202409.1055.v1
Xu, M. Research on Bearing Fault Diagnosis Methods Based on Various Convolutional Neural Network Architectures. Preprints2024, 2024091055. https://doi.org/10.20944/preprints202409.1055.v1
APA Style
Xu, M. (2024). Research on Bearing Fault Diagnosis Methods Based on Various Convolutional Neural Network Architectures. Preprints. https://doi.org/10.20944/preprints202409.1055.v1
Chicago/Turabian Style
Xu, M. 2024 "Research on Bearing Fault Diagnosis Methods Based on Various Convolutional Neural Network Architectures" Preprints. https://doi.org/10.20944/preprints202409.1055.v1
Abstract
Bearing fault diagnosis is crucial for ensuring the stable operation of mechanical equipment. With the continuous development of deep learning technology, Convolutional Neural Networks (CNNs) have demonstrated significant advantages in the field of fault diagnosis. This paper proposes a new method that combines various CNN architectures to improve the accuracy of bearing fault diagnosis. We designed five different convolutional network structures, including SerConv, ResConv, One-Shot Aggregation Convolution (OSAConv), Cross-Stage Aggregation Convolution (CSAConv), and MD-DAConv. Experimental results on the Case Western Reserve University (CWRU) bearing dataset show that the proposed method exhibits high accuracy and robustness in fault diagnosis. The results indicate that strategies such as multi-directional, multi-scale, and residual connections play a crucial role in enhancing the depth and breadth of feature extraction, while simple and effective feature fusion and information transmission mechanisms are key to ensuring the robustness and generalization ability of the model.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
