SLKCResNet: A Lightweight and Highly Robust Bearing Fault Diagnosis Network Based on Separable Large Kernel Residual Convolution
Traditional deep learning approaches for bearing fault diagnosis struggle to balance robustness and computational complexity, thus failing to meet the dual requirements of low complexity and high robustness in practical applications. This paper proposes a novel fault diagnosis model with an innovati...
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| Principais autores: | , , , , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
IEEE
2026-01-01
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| Serier: | IEEE Access |
| Fag: | |
| Online adgang: | https://ieeexplore.ieee.org/document/11357940/ |
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