82 citations · 96 across the 5 of their papers we have counts for
5 papers
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…
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,…
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…
Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis
Jing-Xiao Liao, Hang-Cheng Dong, Zhi-Qi Sun +3
Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits. Recently, machine learning, represented by dee…
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
Jing-Xiao Liao, Bo-Jian Hou, Hang-Cheng Dong +5
Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic functi…