悬链线
受电弓
卷积神经网络
计算机科学
火车
弧(几何)
深度学习
人工智能
人工神经网络
工程类
结构工程
机械工程
地图学
地理
作者
Gülşah Karaduman,Mehmet Karaköse,Erhan Akın
出处
期刊:International Conference on Electrical and Electronics Engineering
日期:2017-11-01
被引量:14
摘要
Pantograph-catenary systems are the most important parts of electric trains. Faults that occur in pantograph-catenary systems seriously affect railway transportation. Arcs are the most important reporters of pantograph-catenary systems. Detection of arcs that give early signal of these faults is very important. In this paper, an approach using deep learning is proposed for the detection of arcs in pantograph-catenary systems. Arc detection is performed using CNN (Convolutional Neural Network). Deep learning have gained great importance in recent years. In this study, experimental results show that the proposed method is quite successful in detecting the arc.
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