most citedA class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network

5 citations · 8 across the 3 of their papers we have counts for

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eess.SP2024★ 2 cited

Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise

Jing-Xiao Liao, Chao He, Jipu Li +3

Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise. Despi…

eess.SP2023★ 5 cited

A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network

Wei-En Yu, Jinwei Sun, Shiping Zhang +2

Deep learning has achieved remarkable success in bearing fault diagnosis. However, its performance oftentimes deteriorates when dealing with highly imbalanced or long-tailed data,…