Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forest

This publication has 28 references indexed in Scilit:

• An Intelligent Fault Diagnosis Method for Imbalanced Nuclear Power Plant Data Based on Generative Adversarial Networks
  Journal of Electrical Engineering & Technology, 2023
• A fault diagnosis of nuclear power plant rotating machinery based on multi-sensor and deep residual neural network
  Annals of Nuclear Energy, 2023
• A Novel Small Samples Fault Diagnosis Method Based on the Self-attention Wasserstein Generative Adversarial Network
  Neural Processing Letters, 2023
• Machine fault diagnosis with small sample based on variational information constrained generative adversarial network
  Advanced Engineering Informatics, 2022
• Fault diagnosis based on conditional generative adversarial networks in nuclear power plants
  Annals of Nuclear Energy, 2022
• Imbalance domain adaptation network with adversarial learning for fault diagnosis of rolling bearing
  Signal, Image and Video Processing, 2022
• Research on robustness of five typical data-driven fault diagnosis models for nuclear power plants
  Annals of Nuclear Energy, 2022
• Analyzing the effect of natural gas, nuclear energy and renewable energy on GDP and carbon emissions: A multi-variate panel data analysis
  Energy, 2021
• Generative adversarial networks for data augmentation in machine fault diagnosis
  Computers in Industry, 2019
• Random forests classifier for machine fault diagnosis
  Journal of Mechanical Science and Technology, 2008