Rotary Machinery Fault Diagnosis Based on Split Attention Mechanism and Graph Convolutional Domain Adaptive Adversarial Network
Author:
Affiliation:
1. Institute of Monitoring and Control of Electromechanical Systems, Xi’an University of Architecture and Technology, Xi’an, China
2. School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, China
Funder
And the Key Laboratory of Road Construction Technology and Equipment (Chang'an University),MOE.
Supported by the Fundamental Research Fundslor the Central Universities, CHD
the Key Research and Development Program of Shaanxi Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/7361/10433867/10384352.pdf?arnumber=10384352
Reference44 articles.
1. Intelligent Fault Diagnosis of Rolling BearingBased on Deep Transfer Learning Using Time-Frequency Representation
2. A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN
3. Applications of machine learning to machine fault diagnosis: A review and roadmap
4. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders
5. Wavelet Support Vector Machine
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