基于2D激光位移传感器的轨底坡动态检测系统研究
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摘要
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.
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.
引文
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[19] Lee J S,Choi S,Kim S S,et al. Displacement estimation from axle-box acceleration using Kalman filter[C]∥in Proc. Autumn Conf.Annu. Meeting Korean Soc. Railway,2009:830-838.
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[21] Lee J S,Choi S,Kim S S,et al. A mixed filtering approach for track condition monitoring using accelerometers on the axle box and bogie[J]. IEEE Transactions on Instrumentation and Measurement,2012,61(3):749-758.
[2]周宇,王少锋,张杰,等.轨底坡对曲线线路钢轨疲劳裂纹萌生寿命的影响[J].中国铁道科学,2005,36(1):25-32.
[3]孟超.激光位移传感器在铁路轮对测量中的应用[C]∥中国空气动力学会测控专业委员会.四届四次空气动力测控技术交流会论文集. 2003:8.
[4]玄柳,何庆,徐华,等.基于双2D激光位移传感器的空间运动物体位置探测[J].电子设计工程,2016,24(12):60-63
[5]李兰君,喻寿益.单点激光三角法测距及其标定[J].仪表技术与传感器,2003(10):49-51.
[6]何先龙.基于伺服式倾角仪的桥梁挠度和转角监测技术的研究[J].国际地震动态,2013(5):44-46.
[7]樊锐,史磊.数字倾角仪的非线性补偿及抗干扰技术[J].仪表技术与传感器,2013(5):36-37,47.
[8]杨亭鹏,刘星桥,陈家斌.光纤陀螺仪(FOG)技术及发展应用[J].火力与指挥控制,2004(2):76-79.
[9]李琳,张旭,屠大维.二维和三维视觉传感集成系统联合标定方法[J].仪器仪表学报,2012,33(11):2473-2479.
[10]张勤,贾庆轩,席宁,等.基于双视点特征匹配的激光-相机系统标定方法[J].仪器仪表学报,2012,33(11):2622-2628.
[11]史红梅,张继科.基于激光三角测量原理的轨距检测系统研究[J].仪器仪表学报,2013,34(9):1934-1940.
[12] Wang Jianhua,Shi Fanhuai,Zhang Jing,et al. A new calibration model of camera lens distortion[J]. Pattern Recognition,2008,41:607-615.
[13]周富强,张广军.表面视觉传感器模型参数的简易标定方法[J].机械工程学报,2005,41(3):175-179.
[14] Zhang Z. A flexible new technique for camera calibration[J]. IEEE Transactions on pattern analysis and machine intelligence,2000,22(11):1330-1334.
[15] Ricolfe-Viala C,Snchez-Salmerón A J. Robust metric calibration of non-linear camera lens distortion[J]. Pattern Recognition,2010,43(4):1688-1699.
[16]魏振忠,张广军,徐园.一种线结构光视觉传感器标定方法[J].机械工程学报,2005,41(2):210-214.
[17]郑锐,王元贵,于龙,等.基于图像处理的振动补偿方法在接触轨检测中的应用[J].城市轨道交通研究,2014,17(1):91-94.
[18]占栋,于龙,邱存勇,等.钢轨轮廓测量中的车体振动补偿问题研究[J].仪器仪表学报,2013,34(7):186-194
[19] Lee J S,Choi S,Kim S S,et al. Displacement estimation from axle-box acceleration using Kalman filter[C]∥in Proc. Autumn Conf.Annu. Meeting Korean Soc. Railway,2009:830-838.
[20] Zhao L,Nie Q,Guo Q. Unscented Kalman filtering for SINS attitude estimation[C]∥Control and Automation,2007. ICCA 2007. IEEE International Conference on. IEEE,2007:228-232.
[21] Lee J S,Choi S,Kim S S,et al. A mixed filtering approach for track condition monitoring using accelerometers on the axle box and bogie[J]. IEEE Transactions on Instrumentation and Measurement,2012,61(3):749-758.