5 citations · 8 across the 3 of their papers we have counts for
3 papers
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,…
cs.LG2023★ 1 cited
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
Jing-Xiao Liao, Sheng-Lai Wei, Chen-Long Xie +5
Deep learning has achieved remarkable success in the field of bearing fault diagnosis. However, this success comes with larger models and more complex computations, which cannot be…