Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault Diagnosis of Rotating Machinery
Author:
Affiliation:
1. School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an, China
2. Key Laboratory of Road Construction Technology and Equipment of Ministry of Education, Chang’an University, Xi’an, China
Funder
Fundamental Research Funds for the Central Universities, Chang’an University
Laboratory of Road Construction Technology and Equipment (Chang’an University), Ministry of Education of the People’s Republic of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10443404.pdf?arnumber=10443404
Reference43 articles.
1. Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications
2. Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review
3. Deep Transfer Diagnosis Method for Machinery in Big Data Era
4. The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest
5. Early Weak Fault Diagnosis of Rolling Bearing Based on Multilayer Reconstruction Filter
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