A Sequential and Asynchronous Federated Learning Framework for Railway Point Machine Fault Diagnosis With Imperfect Data Transmission-Reference-Cited by-同舟云学术

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A Sequential and Asynchronous Federated Learning Framework for Railway Point Machine Fault Diagnosis With Imperfect Data Transmission

Published:2024-06 Issue:6 Volume:20 Page:8828-8837
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Wen Tao¹^ORCID,Chen Xiaohan¹^ORCID,Zhang Dingcheng²^ORCID,Roberts Clive³^ORCID,Cai Baigen¹^ORCID

Affiliation:

1. School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China

2. School of Mechanical Engineering, Sichuan University, Chengdu, China

3. Birmingham Centre for Railway Research and Education, University of Birmingham, Birmingham, U.K.

Funder

National Natural Science Foundation of China

Sichuan Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10550081/10485452.pdf?arnumber=10485452

Reference32 articles.

1. Multiscale Symbolic Lempel–Ziv: An Effective Feature Extraction Approach for Fault Diagnosis of Railway Vehicle Systems

2. Pareto-Based Maintenance Decisions for Regional Railways With Uncertain Weld Conditions Using the Hilbert Spectrum of Axle Box Acceleration

3. Complex Fuzzy System Based Predictive Maintenance Approach in Railways

4. Intelligent Proactive Maintenance System for High-Speed Railway Traction Power Supply System

5. Railway point mechanisms: Condition monitoring and fault detection

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