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Deep learning based Arc detection in pantograph-catenary systems
| Content Provider | Semantic Scholar |
|---|---|
| Author | Karaduman, Gulsah Karakose, Mehmet Ali Akin, Erhan |
| Copyright Year | 2017 |
| Abstract | 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. |
| Starting Page | 904 |
| Ending Page | 908 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.eleco.org.tr/openconf_2017/modules/request.php?a=Accept+as+Lecture&action=view.php&file=1/68.pdf&id=68&module=oc_proceedings |
| Journal | 2017 10th International Conference on Electrical and Electronics Engineering (ELECO) |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
