相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis
Shaowei Liu et al.
MEASUREMENT (2021)
Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty
Xin Gao et al.
NEUROCOMPUTING (2020)
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Wei Zhang et al.
MEASUREMENT (2020)
A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions
Tianhao Hu et al.
MEASUREMENT (2020)
Deep Residual Shrinkage Networks for Fault Diagnosis
Minghang Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Fault diagnosis of electrohydraulic actuator based on multiple source signals: An experimental investigation
Jianyu Wang et al.
NEUROCOMPUTING (2020)
Generative adversarial networks for data augmentation in machine fault diagnosis
Siyu Shao et al.
COMPUTERS IN INDUSTRY (2019)
Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study
Wentao Mao et al.
IEEE ACCESS (2019)
Deep Convolutional Neural Networks
Rafael C. Gonzalez
IEEE SIGNAL PROCESSING MAGAZINE (2018)
Wind turbine blade health monitoring with piezoceramic-based wireless sensor network
Gangbing Song et al.
INTERNATIONAL JOURNAL OF SMART AND NANO MATERIALS (2013)
分享论文
