FedCAE: A New Federated Learning Framework for Edge-Cloud Collaboration Based Machine Fault Diagnosis
Author:
Affiliation:
1. Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Sichuan, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/41/10303745/10122855.pdf?arnumber=10122855
Reference37 articles.
1. Application of Industry 4.0 and Meta Learning for Bearing Fault Classification
2. An Evaluation of the NVIDIA TX1 for Supporting Real-Time Computer-Vision Workloads
3. An Early Classification Approach for Improving Structural Rotor Fault Diagnosis
4. SGDR: Stochastic gradient descent with restarts;loshchilov;Proc Int Conf Learn Representations,0
5. Improved Structural Rotor Fault Diagnosis Using Multi-Sensor Fuzzy Recurrence Plots and Classifier Fusion
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