A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with Multi-source domains

This publication has 33 references indexed in Scilit:

• Multi-Layer domain adaptation method for rolling bearing fault diagnosis
  Signal Processing, 2019
• Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network
  Mechanism and Machine Theory, 2019
• Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
  IEEE Transactions on Industrial Electronics, 2018
• Transferable Representation Learning with Deep Adaptation Networks
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018
• Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning
  IEEE Access, 2018
• Bayesian Networks in Fault Diagnosis
  IEEE Transactions on Industrial Informatics, 2017
• Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines
  Mechanical Systems and Signal Processing, 2017
• A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks
  Mechanical Systems and Signal Processing, 2016
• Domain Adaptation via Transfer Component Analysis
  IEEE Transactions on Neural Networks, 2010
• A Survey on Transfer Learning
  IEEE Transactions on Knowledge and Data Engineering, 2009