A Review of the Characterization of Rolling Contact Fatigue Cracks in Railway Rails Based on Alternating Current Field Measurement

被引：1
|
作者：
Shen J. [1 ,2 ]
Hou Y. [1 ,2 ]
Zhang C. [1 ,2 ]
Wang F. [1 ,2 ]
Liu M. [1 ,2 ]
Meng Z. [1 ,2 ]
Yin W. [3 ]
机构：
[1] Key Laboratory of New Processing Technology for Nonferrous Metals and Materials, Ministry of Education, Guilin University of Technology, Guangxi, Guilin
[2] Guangxi Collaborative Innovation Center for Exploration of Nonferrous Metals and Material Development, Guilin University of Technology, Guangxi, Guilin
[3] School of Electrical, Mechanical and Thermal Engineering, University of Manchester, Greater Manchester, Manchester
来源：
Zhongguo Tiedao Kexue/China Railway Science | 2024年 / 45卷 / 02期
关键词：
Alternating current field measurement; Crack characterization; High-speed rail; Non-destructive testing; Rail; Rolling contact fatigue; Surface breaking crack;
D O I：
10.3969/j.issn.1001-4632.2024.02.02
中图分类号：
学科分类号：
摘要：
Aiming at the problem of rolling contact fatigue (RCF) cracks on the surface of high-speed rails, the formation mechanism and propagation rule of rolling contact fatigue crack on rail surface are analyzed, and the non-destructive testing and characterization methods of high-speed rail crack are systematically summarized. Focusing on the Alternating Current Field Measurement (ACFM) technique, this paper focuses on the development, research status, results from theoretical research and existing problems of ACFM technology, summarizes the characteristics and applications of ACFM technology, and expounds the advantages of ACFM measurement method for the accurate quantitative characterization of rail surface cracks through analyses and comparisons with other non-destructive testing methods. The results show that the pocket depth error of nonuniform crack can be reduced to 5. 5%, the vertical depth error 7. 1%, and the crack cluster error 7. 1% by integrating the ACFM signal compensation algorithm. The crack size of rail RCF is accurately characterized by artificial neural network training, and the error of the final calculation is less than 10%. The development trend of non-destructive detection of rail surface cracks based on artificial intelligence technology is further discussed, and relevant suggestions and prospects are put forward for the subsequent research on the detection and characterization of RCF cracks on rail surface by ACFM technology. © 2024 Chinese Academy of Railway Sciences. All rights reserved.
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页码：15 / 29
页数：14
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