基于2D激光位移传感器的轨底坡动态检测系统研究

Study on Dynamic Detection System of Rail Cant Based on 2D Laser Displacement Sensor

设置合理的轨底坡可使钢轨轨头与车轮踏面合理接触,减轻钢轨轨头的不均匀磨耗,延长钢轨使用寿命。为提高轨底坡静态检测精度,线路日常养护和维修效率,提出一种基于2D激光位移传感器(简称2D)的钢轨廓形检测原理和ARM嵌入式技术的轨底坡动态检测方法。结合传感器工作原理与特点,搭建一套可在线连续检测的轨底坡动态检测系统。考虑到2D空间姿态变化,建立适用于轨底坡动态检测的双2D空间姿态关系模型和标定解算模型。因为车体振动会产生对轨底坡计算结果的影响,利用Kalman滤波算法建立多传感器的状态空间模型,对轨底坡计算结果进行补偿。最后选用GJ-4型轨道检测车进行地铁正线试验,试验结果与人工复核结果的对比,符合工务段要求精度。试验结果验证了该轨底坡动态检测系统切实可行,Kalman滤波算法能够很好地对轨底坡的计算结果进行补偿修正。

Reasonable setting of rail cant can ensure the reasonable contact between rail head and wheel tread,reduce uneven wear of rail head,and therefore prolong service life of the rail. In order to improve the static detection precision of rail cant and the efficiency of daily maintenance,this paper proposes the principle of rail profile detection based on 2 D laser displacement sensor( 2 D for short),and a dynamic detection method based on ARM embedded technology. Based on the working principle and characteristics of sensors, a dynamic detection system for rail cant is built up. In consideration of the spatial attitude variation,a model of dual 2 D spatial attitude relationship, and a calibrationcalculating model are established for dynamic detection of rail cant. The Kalman filter algorithm is used to establish the multi-sensor state space model for compensating the calculation results due to the influence of the body vibration. Finally,a GJ-4 type track inspection vehicle is selected to conduct subway main line test. The comparison of the test results with the manual recheck results conforms to the accuracy requirements by the track maintenance division. The test results verify the feasibility of detection system and prove that the Kalman filtering algorithm can effectively compensate and correct the calculation results.