Bearing fault diagnosis based on DNN using multi-scale feature fusion
Author:
Affiliation:
1. School of Logistic Engineering, Shanghai Maritime University,Shanghai,China
2. School of Software, Henan University,Kaifeng,China
Funder
Natural Science Fund of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9337559/9337493/09337689.pdf?arnumber=9337689
Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Dual-Stream Cross-Modal Feature Fusion Based on Multi-Scale Attention for Industrial Fault Diagnosis;2024 American Control Conference (ACC);2024-07-10
2. A meta-learning method for few-shot bearing fault diagnosis under variable working conditions;Measurement Science and Technology;2024-02-20
3. Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree;Entropy;2023-04-03
4. Trend Feature Consistency Guided Deep Learning Method for Minor Fault Diagnosis;Entropy;2023-01-28
5. Multi-Scale Recursive Semi-Supervised Deep Learning Fault Diagnosis Method with Attention Gate;Machines;2023-01-23
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