行车环境下钢轨轮廓自动配准方法

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。

Automatic registration method of rail profile in train-running environment

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.