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Eddy current quantitative evaluation of high-speed railway contact wire cracks based on neural network
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Xueying Zhou, Wentao Sun, Zehui Zhang, Junbo Zhang, Haibo Chen, Hongmei Li
Railway Sciences | 2024, 3(6) : 764 - 778
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Railway Sciences | 2024, 3(6): 764-778
Research paper
Eddy current quantitative evaluation of high-speed railway contact wire cracks based on neural network
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Xueying Zhou, Wentao Sun, Zehui Zhang, Junbo Zhang, Haibo Chen, Hongmei Li
Affiliations
  • Railway Science and Technology Research and Development Center, China Academy of Railway Sciences Corporation Limited, Beijing, China
  • Power Supply Department, China Railway Zhengzhou Group Corporation Limited, Zhengzhou, China
  • Railway Science and Technology Research and Development Center, China Academy of Railway Sciences Corporation Limited, Beijing, China
Published: 2024-12-10 doi: 10.1108/RS-09-2024-0038
Outline
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Purpose

The purpose of this study is to study the quantitative evaluation method of contact wire cracks by analyzing the changing law of eddy current signal characteristics under different cracks of contact wire of high-speed railway so as to provide a new way of thinking and method for the detection of contact wire injuries of high-speed railway.

Design/methodology/approach

Based on the principle of eddy current detection and the specification parameters of high-speed railway contact wires in China, a finite element model for eddy current testing of contact wires was established to explore the variation patterns of crack signal characteristics in numerical simulation. A crack detection system based on eddy current detection was built, and eddy current detection voltage data was obtained for cracks of different depths and widths. By analyzing the variation law of eddy current signals, characteristic parameters were obtained and a quantitative evaluation model for crack width and depth was established based on the back propagation (BP) neural network.

Findings

Numerical simulation and experimental detection of eddy current signal change rule is basically consistent, based on the law of the selected characteristics of the parameters in the BP neural network crack quantitative evaluation model also has a certain degree of effectiveness and reliability. BP neural network training results show that the classification accuracy for different widths and depths of the classification is 100 and 85.71%, respectively, and can be effectively realized on the high-speed railway contact line cracks of the quantitative evaluation classification.

Originality/value

This study establishes a new type of high-speed railway contact wire crack detection and identification method, which provides a new technical means for high-speed railway contact wire injury detection. The study of eddy current characteristic law and quantitative evaluation model for different cracks in contact line has important academic value and practical significance, and it has certain guiding significance for the detection technology of contact line in high-speed railway.

High-speed railway catenary  /  Crack detection  /  Eddy current detection  /  Neural network
Xueying Zhou, Wentao Sun, Zehui Zhang, Junbo Zhang, Haibo Chen, Hongmei Li. Eddy current quantitative evaluation of high-speed railway contact wire cracks based on neural network[J]. Railway Sciences, 2024 , 3 (6) : 764 -778 . DOI: 10.1108/RS-09-2024-0038
  • "Science and Technology Research and Development Program Project of China Railway Group Co., Ltd"(J2022G016)
Year 2024 volume 3 Issue 6
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Article Info
doi: 10.1108/RS-09-2024-0038
  • Receive Date:2024-09-10
  • Online Date:2026-06-11
  • Published:2024-12-10
Article Data
Affiliations
History
  • Received:2024-09-10
  • Revised:2024-09-29
  • Accepted:2024-09-29
Funding
"Science and Technology Research and Development Program Project of China Railway Group Co., Ltd"(J2022G016)
Affiliations
    Railway Science and Technology Research and Development Center, China Academy of Railway Sciences Corporation Limited, Beijing, China
    Power Supply Department, China Railway Zhengzhou Group Corporation Limited, Zhengzhou, China
    Railway Science and Technology Research and Development Center, China Academy of Railway Sciences Corporation Limited, Beijing, China

Corresponding:

Xueying Zhou can be contacted at:
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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