FedRUL: A New Federated Learning Method for Edge-Cloud Collaboration Based Remaining Useful Life Prediction of Machines-Reference-Cited by-同舟云学术

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FedRUL: A New Federated Learning Method for Edge-Cloud Collaboration Based Remaining Useful Life Prediction of Machines

Published:2023-02 Issue:1 Volume:28 Page:350-359
ISSN:1083-4435
Container-title:IEEE/ASME Transactions on Mechatronics
language:
Short-container-title:IEEE/ASME Trans. Mechatron.

Author:

Guo Liang¹^ORCID,Yu Yaoxiang²^ORCID,Qian Mengui²,Zhang Ruiqi²,Gao Hongli²^ORCID,Cheng Zhe²^ORCID

Affiliation:

1. Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China

2. Engineering Research Center of Advanced Driving Energy-Saving Technology, Southwest Jiaotong University, Chengdu, China

Funder

National Natural Science Foundation of China

Local Development Foundation

Central Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/3516/10045563/09904020.pdf?arnumber=9904020

Reference25 articles.

1. Machinery health indicator construction based on convolutional neural networks considering trend burr

2. Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

3. Federated Machine Learning

4. Collaborative deep learning framework for fault diagnosis in distributed complex systems

5. Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

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