High-Speed Railway Pantograph-Catenary Anomaly Detection Method Based on Depth Vision Neural Network
Author:
Affiliation:
1. Fujian Key Laboratory of New Energy Generation and Power Conversion and the Department of Electrical Engineering, Fuzhou University, Fuzhou, China
2. China Railway Electrification Bureau (Group), Beijing, China
Funder
National Natural Science Foundation of China
Special Project of the Central Government to Guide Local Science and Technology Development
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/9717300/09830591.pdf?arnumber=9830591
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2. Automated anomaly detection of catenary split pins using unsupervised learning;Automation in Construction;2024-09
3. Railway Catenary Condition Monitoring: A Systematic Mapping of Recent Research;Sensors;2024-02-05
4. Research on Key Technologies for Intelligent Detection of High-Speed Railway Pantograph Network Status Based on Improved YOLO Algorithm;2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE);2024-01-12
5. Toward Reliable High-Speed Railway Pantograph-Catenary System State Detection: Multitask Deep Neural Networks With Runtime Reliability Monitoring;IEEE Transactions on Instrumentation and Measurement;2024
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