|3/2014 - 10|
A New Contactless Fault Diagnosis Approach for Pantograph-Catenary System Using Pattern Recognition and Image Processing MethodsAYDIN, I. , KARAKOSE, M. , AKIN, E.
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pantograph, catenary, arcing faults, edge detection, Hough transform, fault diagnosis, railways
pantograph(15), catenary(8), monitoring(7), detection(7), systems(6), power(5), image(5), contact(5), system(4), railways(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2014-08-31
Volume 14, Issue 3, Year 2014, On page(s): 79 - 88
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.03010
Web of Science Accession Number: 000340869800010
SCOPUS ID: 84907314934
Comfort and safety of railway transport has become more important as train speeds continue to increase. In electrified railways, the electrical current of the train is produced by the sliding contact between the pantograph and catenary. The quality of the current depends on the reliability of contact between the pantograph and catenary. So, pantograph inspection is very important task in electrified railways and it is periodically made for preventing dangerous situations. This inspection is operated manually by taking the pantograph to the service for visual anomalies. However, this monitoring is impractical because of time consuming and slowness, as locomotive remains disabled. An innovative method based on image processing and pattern recognition is proposed in this paper for online monitoring of the catenary-pantograph interaction. The images are acquired from a digital line-scan camera. Data are simultaneously processed according to edge detection and Hough transform, and then the obtained features are provided to a D-Markov based state machine, and the pantograph related faults, such as overheating of the pantograph strip, bursts of arcing, and irregular positioning of the contact line are diagnosed. The proposed method is verified by real faulty and healthy pantograph videos.
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 An Efficient Method for High-Speed Railway Dropper Fault Detection Based on Depthwise Separable Convolution, Liu, Shiwang, Yu, Long, Zhang, Dongkai, IEEE Access, ISSN 2169-3536, Issue , 2019.
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Digital Object Identifier: 10.1109/TIM.2018.2884039 [CrossRef]
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Digital Object Identifier: 10.1109/ACCESS.2020.3042535 [CrossRef]
 A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems, Karakose, Ebru, Gencoglu, Muhsin Tunay, Karakose, Mehmet, Yaman, Orhan, Aydin, Ilhan, Akin, Erhan, Journal of Intelligent Manufacturing, ISSN 0956-5515, Issue 4, Volume 29, 2018.
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 Continuous wavelet transform for ferroresonance detection in power systems, Şengüler, Tayfun, Şeker, Serhat, Electrical Engineering, ISSN 0948-7921, Issue 2, Volume 99, 2017.
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 An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection, Chen, Guijun, Zhang, Xueying, Wang, Zizhong John, Li, Fenglian, Mathematical Problems in Engineering, ISSN 1024-123X, Issue , 2015.
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 A new object detection and classification method for quality control based on segmentation and geometric features, Aydin, Ilhan, Karakose, Mehmet, Hamsin, G. Ghazi, Sarimaden, Alisan, Akin, Erhan, 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), ISBN 978-1-5386-1880-6, 2017.
Digital Object Identifier: 10.1109/IDAP.2017.8090172 [CrossRef]
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