An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions

This publication has 43 references indexed in Scilit:

• A new bearing fault diagnosis method based on modified convolutional neural networks
  Chinese Journal of Aeronautics, 2020
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
  Chemistry of Materials, 2019
• A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis
  Neurocomputing, 2019
• Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
  IEEE Transactions on Industrial Electronics, 2018
• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
  IEEE Transactions on Knowledge and Data Engineering, 2018
• Facial expression recognition via learning deep sparse autoencoders
  Neurocomputing, 2018
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
  2017
• Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
  Mechanical Systems and Signal Processing, 2015
• The Graph Neural Network Model
  IEEE Transactions on Neural Networks, 2008
• Bearing fault diagnosis based on wavelet transform and fuzzy inference
  Mechanical Systems and Signal Processing, 2004