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...

詳細記述

書誌詳細

主要な著者:	Qixun Xiao, Yufeng Chen, Min Liu, Abdulrahman Al-Ahmari
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2026-01-01
シリーズ:	IEEE Access
主題:	Ball bearings deep learning fault diagnosis few shot learning machine learning contrastive learning
オンライン･アクセス:	https://ieeexplore.ieee.org/document/11418891/
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