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...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Rongqiang Guan, QiongYing Lv, Bing Jia, Yuhang Lang, Jingjing Yan
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	IEEE 2026-01-01
Sarja:	IEEE Access
Aiheet:	Fault diagnosis robustness lightweight separable large-kernel convolution
Linkit:	https://ieeexplore.ieee.org/document/11357940/
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