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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报

• • 上一篇    

基于自适应时频分析的钢轨连续擦伤周期性病害技术研究

邵  奇,   刘金朝,   郭剑峰,   陶  凯,   杨劲松,   刘竞远   

  1. 中国铁道科学研究院集团有限公司，北京 100081
  • 收稿日期:2022-06-23 接受日期:2023-06-30 发布日期:2025-06-15

Research on Periodic Disease Identification Technology of Continuous Scratch of Rails Based on Adaptive Time-frequency Analysis#br#

SHAO Qi,  LIU Jinchao,  GUO Jianfeng,  TAO Kai,  YANG Jinsong,  LIU Jingyuan   

  1. China Academy of Railway Sciences Group Co., LTD, Beijing 100081
  • Received:2022-06-23 Accepted:2023-06-30 Published:2025-06-15

摘要：

随着铁路运营速度不断提高和运量不断增加，车轮与钢轨相互作用造成的钢轨擦伤现象也愈加突出，用轴箱加速度能有效刻画轨道短波的不平顺，并且擦伤以固定间隔出现，数据会出现周期性波动，针对钢轨连续擦伤的周期特性，提出了周期性轨道病害自适应时频分析法：首先对轴箱加速度数据进行自适应短时傅里叶变换，得到信号的时频能量谱；利用$L^{P}$范数准则提取时间能量信号；再利用移动高斯加权平均滤波器和最小二乘平滑滤波器对时间–能量信号进行平滑和去趋势项处理；最后用固定窗口对平滑信号进行截取后分析及阈值判断，确定连续擦伤区段。对算法进行了现场测试实验，结果表明采用自适应时频分析的方法能够有效识别钢轨连续擦伤区段。

关键词: 自适应时频分析, 时频能量谱, $L^{P}$范数准则, 移动高斯加权平均滤波器, 最小二乘平滑滤波器, 钢轨擦伤

Abstract:

With the continuous increasing of railway operation speed and traffic volume, the phenomenon of rail scratches caused by the interaction between wheels and rails has become more and more prominent. The acceleration of the axle box can effectively describe the short-wave irregularity of the track, and the scratches appear at fixed intervals. There will be periodic fluctuations. In view of the periodic characteristics of continuous rail scratches, we proposes an adaptive time-frequency analysis method for periodic track diseases. First, the adaptive short-time Fourier transform is performed on the acceleration data of the axle box, and the energy spectrum of time-frequency of the signal is obtained. After extracting the time energy signal using the $L^P$ norm criterion, then using the moving Gaussian weighted average filter and the least squares smoothing filter to smooth and detrend the time-energy signal. Finally, using a fixed window to smooth the signal perform post-intersection analysis and threshold judgment determine the continuous abrasion section. The algorithm is tested in the field, and the results show that the method of self-adaptive time-frequency analysis can effectively identify the continuous scratched section of the rail.

Key words: adaptive time-frequency analysis, time-frequency energy spectrum, $L^{P}$ norm criterion, moving Gaussian weighted average filter, least squares smoothing filter, rail scratches

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