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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2014 - 10

 HIGH-IMPACT PAPER 

A New Contactless Fault Diagnosis Approach for Pantograph-Catenary System Using Pattern Recognition and Image Processing Methods

AYDIN, I. See more information about AYDIN, I. on SCOPUS See more information about AYDIN, I. on IEEExplore See more information about AYDIN, I. on Web of Science, KARAKOSE, M. See more information about  KARAKOSE, M. on SCOPUS See more information about  KARAKOSE, M. on SCOPUS See more information about KARAKOSE, M. on Web of Science, AKIN, E. See more information about AKIN, E. on SCOPUS See more information about AKIN, E. on SCOPUS See more information about AKIN, E. on Web of Science
 
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Download PDF pdficon (1,171 KB) | Citation | Downloads: 1,549 | Views: 6,013

Author keywords
pantograph, catenary, arcing faults, edge detection, Hough transform, fault diagnosis, railways

References keywords
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

Abstract
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Full text preview
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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Cited-By CrossRef

[1] Detecting Deformation on Pantograph Contact Strip of Railway Vehicle on Image Processing and Deep Learning, Na, Kyung-Min, Lee, Kiwon, Shin, Seung-Kwon, Kim, Hyungchul, Applied Sciences, ISSN 2076-3417, Issue 23, Volume 10, 2020.
Digital Object Identifier: 10.3390/app10238509
[CrossRef]

[2] Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system, Aydin, Ilhan, Celebi, Selahattin B, Barmada, Sami, Tucci, Mauro, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, ISSN 0954-4097, Issue 1, Volume 232, 2018.
Digital Object Identifier: 10.1177/0954409716662090
[CrossRef]

[3] Condition monitoring of railway pantographs to achieve fault detection and fault diagnosis, Xin, Tingyu, Roberts, Clive, Weston, Paul, Stewart, Edward, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, ISSN 0954-4097, Issue 3, Volume 234, 2020.
Digital Object Identifier: 10.1177/0954409718800567
[CrossRef]

[4] A Novel Ultrasonic Guided Wave-Based Method for Railway Contact Wire Defect Detection, Chang, Yong, Li, Nana, Zhao, Jiyuan, Wang, Yu, Yang, Zhe, IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, Issue , 2022.
Digital Object Identifier: 10.1109/TIM.2022.3178492
[CrossRef]

[5] 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.
Digital Object Identifier: 10.1109/ACCESS.2019.2942079
[CrossRef]

[6] A new approach based on firefly algorithm for vision-based railway overhead inspection system, Aydin, Ilhan, Measurement, ISSN 0263-2241, Issue , 2015.
Digital Object Identifier: 10.1016/j.measurement.2015.07.022
[CrossRef]

[7] Modeling and analysis of transition-point disappearance in high-speed railway pantograph-catenary system, Chen, Junqing, Liu, Jidong, Guan, Jinfa, Han, Feng, Wu, Jiqing, Chen, Weirong, Advances in Mechanical Engineering, ISSN 1687-8132, Issue 11, Volume 15, 2023.
Digital Object Identifier: 10.1177/16878132231210083
[CrossRef]

[8] Pantograph–catenary electrical contact system of high-speed railways: recent progress, challenges, and outlooks, Wu, Guangning, Dong, Keliang, Xu, Zhilei, Xiao, Song, Wei, Wenfu, Chen, Huan, Li, Jie, Huang, Zhanglin, Li, Jingwei, Gao, Guoqiang, Kang, Guozheng, Tu, Chuanjun, Huang, Xingyi, Railway Engineering Science, ISSN 2662-4745, Issue 4, Volume 30, 2022.
Digital Object Identifier: 10.1007/s40534-022-00281-2
[CrossRef]

[9] Dynamic target template matching for railway catenary suspension motion detection in wind area, Zhou, Wei, Xiao, Heting, Wang, Zhonggang, Chen, Lin, Fu, Shaoqing, International Journal of Distributed Sensor Networks, ISSN 1550-1477, Issue 9, Volume 14, 2018.
Digital Object Identifier: 10.1177/1550147718797956
[CrossRef]

[10] A Robust Pantograph–Catenary Interaction Condition Monitoring Method Based on Deep Convolutional Network, Zhang, Dongkai, Gao, Shibin, Yu, Long, Kang, Gaoqiang, Zhan, Dong, Wei, Xiaoguang, IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, Issue 5, Volume 69, 2020.
Digital Object Identifier: 10.1109/TIM.2019.2920721
[CrossRef]

[11] Online Intelligent Perception of Pantograph and Catenary System Status Based on Parameter Adaptation, Shen, Yuan, Pan, Xiao, Chang, Luonan, Applied Sciences, ISSN 2076-3417, Issue 4, Volume 11, 2021.
Digital Object Identifier: 10.3390/app11041948
[CrossRef]

[12] PAC Interaction Inspection Using Real-Time Contact Point Tracking, Shen, Yuan, Liu, Zhen, Zhang, Guangjun, IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, Issue 10, Volume 68, 2019.
Digital Object Identifier: 10.1109/TIM.2018.2884039
[CrossRef]

[13] Online Pantograph-Catenary Contact Point Detection in Complicated Background Based on Multiple Strategies, Yang, Xuan, Zhou, Ning, Liu, Yueping, Quan, Wei, Lu, Xuemin, Zhang, Weihua, IEEE Access, ISSN 2169-3536, Issue , 2020.
Digital Object Identifier: 10.1109/ACCESS.2020.3042535
[CrossRef]

[14] The online monitoring system of pantograph slider based on 2D laser displacement sensors, Yao, Xiaowen, Xing, Zongyi, Zhang, Zhenyu, Sheng, Andong, Measurement, ISSN 0263-2241, Issue , 2022.
Digital Object Identifier: 10.1016/j.measurement.2022.111083
[CrossRef]

[15] Railway Catenary Condition Monitoring: A Systematic Mapping of Recent Research, Chen, Shaoyao, Frøseth, Gunnstein T., Derosa, Stefano, Lau, Albert, Rönnquist, Anders, Sensors, ISSN 1424-8220, Issue 3, Volume 24, 2024.
Digital Object Identifier: 10.3390/s24031023
[CrossRef]

[16] 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.
Digital Object Identifier: 10.1007/s10845-015-1136-3
[CrossRef]

[17] The enhancement of catenary image with low visibility based on multi-feature fusion network in railway industry, Chen, Yuwen, Song, Bin, Du, Xiaojiang, Guizani, Nadra, Computer Communications, ISSN 0140-3664, Issue , 2020.
Digital Object Identifier: 10.1016/j.comcom.2020.01.040
[CrossRef]

[18] Continuous wavelet transform for ferroresonance detection in power systems, Şengüler, Tayfun, Şeker, Serhat, Electrical Engineering, ISSN 0948-7921, Issue 2, Volume 99, 2017.
Digital Object Identifier: 10.1007/s00202-016-0387-0
[CrossRef]

[19] Pantograph Arc Location Estimation Using Resonant Frequencies in DC Railway Power Systems, Fan, Fulin, Wank, Andreas, Seferi, Yljon, Stewart, Brian G., IEEE Transactions on Transportation Electrification, ISSN 2332-7782, Issue 4, Volume 7, 2021.
Digital Object Identifier: 10.1109/TTE.2021.3062229
[CrossRef]

[20] Experimental research on the non-uniform wear of the carbon strip of the metro pantograph, Luo, Xu, Cai, Cheng, Yang, Hongjuan, Mei, Guiming, Gao, Chang, Liu, Weiping, Yang, Dongsheng, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, ISSN 1350-6501, Issue 10, Volume 237, 2023.
Digital Object Identifier: 10.1177/13506501231193068
[CrossRef]

[21] 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.
Digital Object Identifier: 10.1155/2015/638926
[CrossRef]

[22] Measuring the geometry of catenary devices, Dobrynin, E. V., Kharitonova, T. V., Kudryashov, D. A., THE 6TH INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT, EPIDEMIOLOGY AND INFORMATION SYSTEM (ICENIS) 2021: Topic of Energy, Environment, Epidemiology, and Information System, ISBN , Issue , 2023.
Digital Object Identifier: 10.1063/5.0105972
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[23] Automatic Pantograph Health Status Report Generation Based on Dense Captioning, Yin, Xinqiang, Wei, Xiukun, Li, Zhaoxin, Wei, Dehua, Tang, Qingfeng, 2022 14th International Conference on Advanced Computational Intelligence (ICACI), ISBN 978-1-6654-7045-2, 2022.
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[24] 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.
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[CrossRef]

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