Shuffle-fusion pyramid network for bearing fault diagnosis under noisy environments
Cheng Zhao,
Linfeng Deng,
Yuanwen Zhang
et al.
Abstract:Recent advancements in deep learning have propelled the exploration of big data-driven fault diagnosis techniques. Nevertheless, traditional models often suffer from prohibitive computational demands, rendering them impractical for on-site deployment in rolling bearing fault diagnosis. To address this challenge, this paper introduces a novel lightweight fault diagnosis model with the pyramid architecture, named Shuffle-Fusion Pyramid Network (Shuffle-FPN). The model heralds several innovations: (1) A pyramid s… Show more
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