Joint Denoising and Classification for Fault Diagnosis Under Strong Noise

Industrial fault diagnosis often degrades when vibration signals are strongly contaminated by noise and only a few labeled samples are available. This paper proposes a joint denoising and classification framework for rolling bearing and gearbox diagnosis. An adaptive denoiser combines band-wise Wien...

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Hlavní autoři:	Qixun Xiao, Yufeng Chen, Min Liu, Abdulrahman Al-Ahmari
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2026-01-01
Edice:	IEEE Access
Témata:	Ball bearings deep learning fault diagnosis few shot learning machine learning contrastive learning
On-line přístup:	https://ieeexplore.ieee.org/document/11418891/
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