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A Novel Detection Method of Rail Insulation Defects based on FFC-Swin-Transformer
ID：134 View Protection：ATTENDEE Updated Time：2025-11-03 11:45:38 Hits：336 Oral Presentation

Start Time：2025-11-09 11:20（Asia/Shanghai）

Duration：15min

Session：S5 5.AI-driven technology » S55.AI-driven technology

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Abstract
As urban rail transit grows, stray current seriously threatens the safety of power systems and oil/gas pipelines. Rail-to-earth insulation the key to control stray current. Aiming at the problem that it is difficult to detect and locate the rail insulation defects of DC traction power supply system, a novel rail insulation defect detection method based on the FFC-Swin-Transformer is proposed. By establishing a four-layer "catenary-rail-SCCN-earth" equivalent circuit model and integrating multi-train operation conditions, a rail potential dataset is efficiently generated via parallel computing. Time-domain signals of rail potential are converted to frequency-domain features using Fourier transform, and the fused time-frequency information is fed into an improved FFC-Swin-Transformer network to achieve accurate detection of rail insulation states. Experimental results show that after training on 22496 sample groups, the model achieves a test accuracy of 82.95%, effectively identifying section insulation defects and exhibiting promising engineering application potential.
Keywords
DC metro system,stary current,defect detection,rail potential,rail insulation
Speaker
Feilong Liu
Southwest Jiaotong University

Submission Author
Feilong Liu Southwest Jiaotong University
Wei Liu Southwest Jiaotong University
Shuangrui Yang Southwest Jiaotong University
Zhuoxin Yang Southwest Jiaotong University
Yuning Tang Southwest Jiaotong University
Songyuan Li Southwest Jiaotong University
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Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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