Dual-Path Mixed-Domain Residual Threshold Networks for Bearing Fault Diagnosis
Author:
Affiliation:
1. Department of Automation, Zhejiang University of Technology, Hangzhou, China
2. School of Transportation Science and Engineering, Beihang University, Beijing, China
3. Beijing Normal University at Zhuhai, Zhuhai, China
Funder
Natural Science Foundation of Zhejiang Province
Science and Technology Innovation in Ningbo City
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/41/9970766/09693342.pdf?arnumber=9693342
Reference32 articles.
1. Multibranch and Multiscale CNN for Fault Diagnosis of Wheelset Bearings Under Strong Noise and Variable Load Condition
2. Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads
3. An optimal deep sparse autoencoder with gated recurrent unit for rolling bearing fault diagnosis
4. Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability
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