机车轮对的检修是十分重要的，轮缘磨耗、踏面磨耗等是重要的测量参数。
To examine locomotive wheels is very important, brim abrasion and fade abrasion of the wheels are the important parameters.
论文主要研究基于图像处理与机器视觉的轮对踏面磨耗和相关参数的自动检测技术。
The technology based on image treatment and machine vision for wear detection and correlation parameters measurement of the wheelset tread is presented in this paper.
车轮踏面磨耗导致车轮外形改变，使其滚动圆直径产生偏差，对车辆系统的动力学特性影响较大。
Wheel tread wear leads to change of the wheel profile and rolling diameter, which influences the dynamic behaviors of railway vehicles seriously.
采用数值仿真方法研究了不同车轮踏面外形、轮对内侧距、轨底坡和车速对踏面磨耗深度和磨耗分布的影响。
Numerical simulation was used to study the influence of profile shape, vehicle speed, wheel back distance and rail cant on the wear depth and wear distribution of wheel profile.
该方法能自动判断被测轮对轮缘踏面是否因磨耗过限而需要旋修。
This method can automatically judge whether the wheel profile needs to repair because of wears and tears.
与新用车轮踏面相比，测试得到的磨耗后车轮踏面在其名义直径位置凹陷区域附近容易形成踏面两点接触。
Compared to the new wheel tread, the worn tread is easy to form two-point contact nearby the depression area of the nominal diameter position of wheel tread.
与新用车轮踏面相比，测试得到的磨耗后车轮踏面在其名义直径位置凹陷区域附近容易形成踏面两点接触。
Compared to the new wheel tread, the worn tread is easy to form two-point contact nearby the depression area of the nominal diameter position of wheel tread.
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