行车环境下钢轨轮廓自动配准方法

王昊; 王胜春; 王卫东

doi:10.13700/j.bh.1001-5965.2018.0050

行车环境下钢轨轮廓自动配准方法

doi: 10.13700/j.bh.1001-5965.2018.0050

王昊^1, ,,
王胜春^{1, 2},
王卫东¹

1.
中国铁道科学研究院, 北京 100081
2.
中国铁道科学研究院基础设施检测研究所, 北京 100081

基金项目:

中国铁路总公司科技研究开发计划 J2016G003

中国铁路总公司重大科研计划 2015G001-B

北京市科技计划 D17110600060000

详细信息

作者简介:
王昊  男, 博士研究生, 副研究员。主要研究方向:机器视觉、结构光测量

王胜春  男, 博士。主要研究方向:机器视觉、图像处理、缺陷检测

王卫东  男, 博士, 教授, 博士生导师。主要研究方向:铁道工程、结构光测量

通讯作者:
王昊, E-mail:wanghao1984@rails.cn

中图分类号: U216.3
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出版历程
- 收稿日期: 2018-01-19
- 录用日期: 2018-02-10
- 网络出版日期: 2018-11-20

Automatic registration method of rail profile in train-running environment

WANG Hao^{1
, ,},
WANG Shengchun^{1, 2},
WANG Weidong¹

1.
China Academy of Railway Sciences, Beijing 100081, China
2.
Infrastructure Inspection Research Institute, China Academy of Railway Sciences, Beijing 100081, China

Funds:

The Research and Development Plan of Science and Technology of Chinese Railway Corporation J2016G003

Major Research Plan of Chinese Railway Corporation 2015G001-B

Beijing Science and Technology Planning Project D17110600060000

More Information

Corresponding author: WANG Hao, E-mail:wanghao1984@rails.cn

摘要

摘要:
针对行车环境下列车晃动和环境噪声对钢轨磨耗测量的影响，提出了一种轨腰小圆弧自动提取方法，实现了钢轨轮廓的高精度配准。首先，提出了基于截断残差直方图的多项式拟合方法，寻找廓形最优拟合曲线，降低了噪声对轮廓拟合的影响；然后，针对拟合曲线的曲率分布特征，提出了基于动态窗口的最大曲率熵区间搜索算法实现轨腰小圆弧的自动分割；最后，基于两侧轨腰小圆弧拟合2个圆心作为匹配基准点，实现钢轨测量轮廓与标准设计轮廓的对齐配准。静态实验结果表明，该方法的系统测量误差均值和标准差都控制在0.01 mm之内，具有较小的测量误差和良好的重复性。现场动态测量也验证了该方法在行车环境下的重复性精度，多次测量结果的重复性良好，钢轨磨耗动态测量偏差控制在0.2 mm以内。
- 钢轨磨耗 /
- 轮廓配准 /
- 曲率熵 /
- 结构光 /
- 行车环境
Abstract:
Aimed at the influence of train vibration and environmental noise on the measurement of track wear, we proposed a method for automatic extraction of small circular arc of the rail waist, and achieved the high-precision registration of rail profile. First, a polynomial fitting method based on truncated residual histogram was proposed to find the optimal fitting curve of the profile and reduce the influence of noise on profile curve fitting; Then, aimed at the curvature distribution characteristics of the fitting curve, the interval search algorithm based on dynamic window's maximum curvature entropy was proposed to realize the automatic segmentation of small circular arc; Finally, two circle centers were fitted as matching reference points based on small circular arcs on both sides, and the alignment from the measured profile to the standard designed profile was realized. The static experimental results show that the mean value of system measurement error and standard deviation of the method are controlled within 0.01 mm with small measurement errors and high repeatability. The dynamic test also verified the repeatability accuracy of the method in the train-running environment, and the dynamic measurement deviation of rail wear is within 0.2 mm with high repeatability.
- rail wear /
- profile registration /
- curvature entropy /
- structured light /
- train-running environment

HTML全文

图 1 钢轨磨耗测量原理

Figure 1. Schematic diagram of rail wear measurement

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图 2 钢轨轮廓结构及各区段曲率分布

Figure 2. Rail profile structure and curvature distribution in each section

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图 3 轨廓噪声

Figure 3. Rail profile noise

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图 4 不同阶次的轮廓点拟合结果

Figure 4. Fitting results of profile points with different orders

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图 5 残差统计直方图

Figure 5. Residual statistics histogram

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图 6 截断残差直方图与正态分布

Figure 6. Truncated residual histogram and normal distribution

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图 7 标准轨的轨腰轮廓曲率

Figure 7. Curvature of standard rail waist profile

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图 8 曲率熵变化分布

Figure 8. Variation distribution of curvature entropy

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图 9 基于动态窗口的最大曲率熵区间搜索

Figure 9. Maximum curvature entropy interval search based on dynamic window

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图 10 实验环境与结构光图像

Figure 10. Experimental environment and structured light image

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图 11 实测标准轨的小圆区域分割及圆心拟合结果

Figure 11. Results of small circle area segmentation and circle center fitting for actual measurement standard rail

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图 12 基于左右侧小圆圆心的标准轨轮廓对齐

Figure 12. Alignment of standard rail profile on left and right side of small circle center

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图 13 标准轨廓形对比

Figure 13. Standard rail profile comparison

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图 14 系统测量误差及静态重复性精度

Figure 14. System measurement error and static repeatability accuracy

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图 15 噪声干扰轨廓数据的小圆分割和圆心拟合结果

Figure 15. Small circle segmentation and circle center fitting result of rail profile with noise interference

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图 16 动态测量中拟合小圆的半径变化

Figure 16. Radius variation of small fitting circle with dynamic measurement

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图 17 钢轨磨耗测量的动态重复性精度

Figure 17. Dynamic repeatability accuracy of rail wear measurement

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表 1 空间轮廓点多项式拟合误差统计

Table 1. Error statistics of polynomial fitting of spatial profile points

拟合阶次	平方和误差	确定系数	均方根误差
1	20 862	0.393 4	12.34
2	6 516	0.807 8	6.922
3	1 389	0.959 6	3.207
4	51.93	0.998 5	0.623
5	53.07	0.998 5	0.632
6	67.14	0.998	0.713
7	79.42	0.997 7	0.779
8	92.69	0.997 3	0.844
9	104.8	0.997	0.901

下载: 导出CSV

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