Enhancing crack detection in railway tracks through AI-optimized ultrasonic guided wave modes

Abstract

The utilization of ultrasonic guided wave technology for detecting cracks in railway tracks involves analyzing echo signals produced by the interaction of cracks with guided wave modes to achieve precise crack localization, which is extremely important in a real-time railway crack robotic detection system. Addressing the challenge of selecting the optimal detection mode for cracks in various regions of railway tracks, this paper presents a method for optimal crack detection mode selection. This method is based on the sensitivity of guided wave modes to cracks. By examining the frequency dispersion characteristics and mode shapes of guided wave modes, we establish indicators for crack zone energy and crack reflection intensity. Our focus is on the railhead of the railway track, selecting guided wave modes characterized by specific cracks for detection purposes. Experimental findings validate the accuracy of our proposed mode selection method in detecting cracks in railway tracks. This research not only enhances crack detection but also lays the groundwork for exploring advanced detection and localization techniques for cracks in railway tracks.

Keywords

Sensitivity / Crack / Guided / Modal

CRediT authorship contribution statement

Jianjun Liu: Resources, Data curation, Conceptualization. Huan Luo: Writing - original draft, Funding acquisition. Han Hu: Writing - original draft, Methodology. Jian Li: Resources, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work is suported by Natural Science Foundation of Guangdong Province, China (2022A1515011409 and 2023A1515011253); supported by Key Areas Special Project of General Universities in Guangdong Province, China(2023ZDZX1024); supported in part by research grants from The Youth Project of National Natural Science Foundation of China (52105268); supported in part by the Key Project of Shaoguan University, China (SZ2020KJ02); supported in part by Youth Project of National Natural Science Foundation of China (62001200), supported in part by the Natural Science Foundation of Fujian Province, China (2020J01817); supported by Shaoguan Social Development Science and Technology Collaborative Innovation System Construction Project, China (230330178036242 and 230330098033679); supported in part by Shaoguan University Ph.D. Initiation Project: Research on the Consistency Problem of Nonlinear Multi-agent Systems, China (440-9900064604); supported in part by 2024 Key Scientific Research Project of Shaoguan University: Research on Autonomous Exploration System of Rescue Robot, China; supported in part by the Higher education institution featured innovation project of Department of Education of Guangdong Province, China (2023KTSCX138); supported in part by the Natural Science Foundation of Chongqing, China (CSTB2022NSCQMSX1386); supported in part by Shaoguan University Ph.D. Initiation Project, China (440-9900064602); supported in part by Guangdong Province Key Construction Discipline Research Capacity Enhancement Project (2022ZDJS051, 2021ZDJS070).

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