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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★ 2 cited
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
Xiao-Cong Zhong, Qisong Wang, Dan Liu +5
Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the classification is affected by the non-stationarity and i…
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,…