摘要
钢轨焊缝作为轨道的三大薄弱环节之一,其服役状态对于铁路运营的安全性与稳定性具有重要影响,因此,实现钢轨焊缝几何平直度的快速、高效测量,对于保障铁路行车安全,提升铁路工务人员作业效率具有重要意义。鉴于此,首先利用手推式钢轨短波几何检测装备采集钢轨短波不平顺波形信息;然后,提出一种基于模糊瓦片编码神经网络的深度学习方法,该方法不仅能够输出短波不平顺信号中钢轨焊缝的中心里程位置还可以计算相应位置的识别可靠度,从而实现从多种混合、复杂短波不平顺信号中快速分离出钢轨焊缝的几何波形,提升工务数据利用效率;最后,在某线路开展现场测试,验证该方法的准确性与稳定性,同时将焊缝平直度的检测结果与工务部门使用的电子平直尺测量结果进行对比,提升工程可行性。研究结果表明:1)基于瓦片编码网络的钢轨焊缝识别准确率可达92.01%,召回率可到94.98%;2)同时,基于瓦片编码网络能够准确识别钢轨焊缝中心,与实际焊缝中心偏差可控制在0.03 m以内;3)最终识别焊缝的1 m弦平直度与现场所使用的标准钢直尺+塞尺组合测量幅值结果基本一致,最大幅值相差不超过0.1 mm。综上所述,该研究可为提升工务检测数据的利用效率,降低钢轨焊缝的检测成本提供一定的工程技术参考价值。
Rail welds,as one of the three vulnerable components of the track,can affect the safety and stability of railway operations.Thus,the rapid and efficient measurement of the geometric irregularity of rail welds is crucial for ensuring the safety of railway traffic and enhancing the work efficiency of railway maintenance personnel.In this regard,this study initially utilized a hand-pushed rail short-wave geometric detection device to collect the short-wave track irregularity.Subsequently,a deep learning approach based on the fuzzy tile coding neural network was proposed,which could not only determine the centerline mileage of the rail welds from the shortwave irregularity signals but also calculate the reliability of the identification at corresponding positions.This enabled the swift separation of the geometric waveform of rail welds from a variety of mixed and complex shortwave track irregularity signals,thereby improving the efficiency of data utility.Finally,field experiments were conducted on one railway line to verify the accuracy and stability of the proposed method,and the results of the weld irregularity were compared with the standard measurement using a MCRuler straightedge by the maintenance department,to enhance the practicality of the engineering application.The findings of this research can be concluded as follows.(1)The identification accuracy of rail welds based on the tile coding network can reach 92.01%,with a recall rate of up to 94.98%.(2)Moreover,the tile coding network can accurately pinpoint the center of the rail welds,with the deviation from the actual weld center kept within 0.03 m.(3)The final results of the 1-meter-chord irregularities of the rail welds is fundamentally consistent with the amplitude results obtained from the MCRuler straightedges,with the maximum amplitude difference not exceeding 0.1 mm.Overall,this study can offer valuable engineering technical reference for enhancing the efficiency of maintenance inspection data utilization and reducing the detection costs associated with rail welds.
作者
高天赐
史一帆
江乐鹏
王源
刘晓舟
罗钦
王平
GAO Tianci;SHI Yifan;JIANG Lepeng;WANG Yuan;LIU Xiaozhou;LUO Qin;WANG Ping(College of Urban Transportation and Logistics,Shenzhen Technology University,Shenzhen 518118,China;College of Applied Sciences,Shenzhen University,Shenzhen 518060,China;MOE Key Laboratory of High-Speed Railway Engineering,Southwest Jiaotong University,Chengdu 610031,China;School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China;Eborail Technology Co.,Ltd.,Shenzhen 518052,China)
出处
《铁道科学与工程学报》
北大核心
2025年第5期2346-2354,共9页
Journal of Railway Science and Engineering
基金
国家自然科学基金资助项目(52008198,52208441)
广东省普通高校创新团队资助项目(2022KCXTD027)。
关键词
钢轨焊缝
瓦片编码网络
短波不平顺
智能检测
焊缝平直度
rail weld
tile-coding-based neural networks
short-wave track irregularity
intelligent inspection
rail weld irregularity
