Attention-Aware Meta-Reweighted Optimization for Enhanced Intelligent Fault Diagnosis
Author:
Affiliation:
1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China
2. Commercial Aircraft Corporation of China, Ltd., Shanghai, China
Funder
National Natural Science Foundation of China
China Aviation Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10520896.pdf?arnumber=10520896
Reference40 articles.
1. Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings
2. Knowledge distilling based model compression and feature learning in fault diagnosis
3. Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath–Geva clustering algorithm without principal component analysis and data label
4. Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions
5. Fault diagnosis method of rolling bearing based on multiple classifier ensemble of the weighted and balanced distribution adaptation under limited sample imbalance
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