Deep meta-learning-based multi-signal data fusion approach for fault diagnosis

This publication has 39 references indexed in Scilit:

• Condition-based maintenance using machine learning and role of interpretability: a review
  International Journal of System Assurance Engineering and Management, 2022
• Few-shot fault diagnosis of rolling bearing under variable working conditions based on ensemble meta-learning
  Digital Signal Processing, 2022
• Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
  Artificial Intelligence Review, 2022
• A systematic review of data-driven approaches to fault diagnosis and early warning
  Journal of Intelligent Manufacturing, 2022
• Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review
  Applied Intelligence, 2022
• Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
  Applied Intelligence, 2022
• Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks
  Journal of Intelligent Manufacturing, 2022
• A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images
  Neural Computing and Applications, 2022
• A survey of deep meta-learning
  Artificial Intelligence Review, 2021
• CBAM: Convolutional Block Attention Module
  2018