Volume 26 Issue 9
Sep.  2026
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2026  >  26(9): 191-206
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XU Peng, QIN Zhi-jian, YANG Yong-han, SHAN Xiao-nian, WANG Wen-fei, ZHANG Zi-jun. Real-time monitoring method for bolt loosening in railway hump retarders based on top-down strategy[J]. Journal of Traffic and Transportation Engineering, 2026, 26(9): 191-206. doi: 10.19818/j.cnki.1671-1637.2026.270
Citation: XU Peng, QIN Zhi-jian, YANG Yong-han, SHAN Xiao-nian, WANG Wen-fei, ZHANG Zi-jun. Real-time monitoring method for bolt loosening in railway hump retarders based on top-down strategy[J]. Journal of Traffic and Transportation Engineering, 2026, 26(9): 191-206. doi: 10.19818/j.cnki.1671-1637.2026.270
shu

Real-time monitoring method for bolt loosening in railway hump retarders based on top-down strategy

doi: 10.19818/j.cnki.1671-1637.2026.270
  • 1.

    College of Civil and Transportation Engineering, Hohai University, Nanjing 210024, Jiangsu, China

  • 2.

    College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China

Funds:

National Natural Science Foundation of China 52572341

More Information
  • Corresponding author: XU Peng, associate professor, PhD, E-mail: xp@hhu.edu.cn
  • Received Date: 2025-12-02
  • Accepted Date: 2026-03-20
  • Rev Recd Date: 2026-02-20
  • Publish Date: 2026-09-28
    Abstract

  • To achieve automated and accurate monitoring of bolt loosening states in railway hump retarders under complex operating conditions, including continuous impact vibration, weather variations, and occlusion caused by passing trains, a real-time monitoring method for bolt loosening in railway hump retarders based on a top-down strategy was proposed. A three-stage collaborative process comprising detection, localization, and calculation was adopted. Accurate localization of bolt surface regions was achieved using the RTMDet model. Six corner points of each bolt were then identified within the detection box using the RTMPose model. An algorithm including geometric validity determination, perspective distortion correction, and angle normalization based on symmetry constraints was further proposed. The algorithm was used to quantitatively calculate the rotation angle and determine the bolt state. To verify the effectiveness of the method, high-definition camera equipment was deployed at a railway marshalling yard. A bolt image dataset was constructed under various weather conditions, including sunny, cloudy, rainy, snowy, and hazy conditions, and at different times of day, including morning, evening, and night. The dataset also included images with localized oil contamination and from multiple viewing angles. Comparative experiments were performed to evaluate the proposed method. Research results show that the proposed method achieves an accuracy of 95.1% and a recall of 93.8%. For bolt corner-point localization, the method achieves a root-mean-square error of 1.25 pixels and a coefficient of determination of 0.952 for rotation-angle estimation, with an average processing time of 28.3 ms per frame. The method demonstrates good robustness and generalization ability under varying installation angles, occlusion conditions, and illumination conditions, with angular errors below 1°. The proposed method provides a feasible technical solution for high-accuracy, real-time visual monitoring of bolt loosening in complex railway environments. This approach can be applied to intelligent operation and maintenance systems and condition monitoring systems for critical railway equipment, such as retarders and turnouts in marshalling yards.

     

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