基于激光轮廓仪的非接触式钢轨廓形检测系统

doi:10.16180/j.cnki.issn1007-7820.2020.08.005

电子科技 ›› 2020, Vol. 33 ›› Issue (8): 28-33.doi: 10.16180/j.cnki.issn1007-7820.2020.08.005

基于激光轮廓仪的非接触式钢轨廓形检测系统

包亚俊¹,刘瑾¹,杨海马²,江声华³,袁宝龙⁴,杨萍⁵

1.上海工程技术大学电子电气工程学院,上海 201620
2.上海理工大学光电信息与计算机工程学院,上海 200093
3.上海瑞纽机械股份有限公司,上海 200120
4.上海淮科智能科技有限公司,上海 201999
5.上海航天设备制造总厂有限公司,上海 200245

收稿日期:2019-05-27 出版日期:2020-08-15 发布日期:2020-08-24
作者简介:包亚俊(1992-),男,硕士研究生。研究方向:智能控制与智能检测。|刘瑾(1978-),女,博士,副教授。研究方向:光电检测及智能监控。|杨海马(1979-),男,博士,副教授。研究方向:光电检测及信号处理。
基金资助:
国家自然科学基金(61701296);国家自然科学基金(U1831133);上海市自然科学基金(17ZR1443500);上海航天科技创新基金(SAST2017-062);宝山区科技创新专项基金(17-C-21)

Non-contact Rail Profile Detection System Based on Laser Profiler

BAO Yajun¹,LIU Jin¹,YANG Haima²,JIANG Shenghua³,YUAN Baolong⁴,YANG Ping⁵

1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
2. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
3. Shanghai Ruilnu Machinery Corp.,Shanghai 200120,China
4. Shanghai Huaike Intelligent Technology Co., Ltd.,Shanghai 201999,China
5. Shanghai AerospaceEquipments Manufacturer Co.,Ltd.,Shanghai 200245,China

Received:2019-05-27 Online:2020-08-15 Published:2020-08-24
Supported by:
National Natural Science Foundation of China(61701296);National Natural Science Foundation of China(U1831133);Shanghai Natural Science Foundation(17ZR1443500);Shanghai Aerospace Science and Technology Innovation Fund(SAST2017-062);Baoshan District Science and Technology Innovation Special Fund(17-C-21)

摘要/Abstract

摘要：

传统钢轨廓形几何尺寸测量多以手工测量为主,基于激光轮廓仪的高精度廓形检测系统的应用可解决手工测量效率低、误差大的问题。新补充利用中值滤波法对激光轮廓仪采集到的廓形数据进行预处理;通过坐标平移和坐标旋转将坐标系归一化;然后通过二次拟合选择合适的特征点对廓形拼接匹配。经现场测试,钢轨廓形尺寸的重复测量误差小于0.1 mm,绝对测量精度最高可达0.014 mm,满足国家标准对钢轨廓形检测精度的需要,说明该方法有效提高了钢轨的检测效率。

关键词: 钢轨廓形, 中值滤波法, 坐标变换, 激光轮廓仪, 廓形检测, 二次拟合

Abstract:

At present, the geometrical dimension measurement of the rail profile is mostly based on manual measurement. The high-precision profile detection system based on the laser profiler can improve work efficiency of manual measurement and reduce measurement errors. The median filtering method was used to preprocess the profile data collected by the laser profilometer, and the coordinate system was transformed by coordinate translation and coordinate rotation. Then, the appropriate feature point was selected by quadratic fitting to match the silhouette stitching. After field test, the repeated measurement error of the rail profile size was less than 0.1 mm, and the absolute measurement accuracy was up to 0.014 mm. These experimental value met the requirements of the national standard for the detection accuracy of the rail profile, indicating the proposed method could effectively improve the detection efficiency of the rail.

Key words: rail profile, median filtering, coordinate transformation, laser profiler, profile detection, quadratic fitting

中图分类号:

TP278

包亚俊,刘瑾,杨海马,江声华,袁宝龙,杨萍. 基于激光轮廓仪的非接触式钢轨廓形检测系统[J]. 电子科技, 2020, 33(8): 28-33.

BAO Yajun,LIU Jin,YANG Haima,JIANG Shenghua,YUAN Baolong,YANG Ping. Non-contact Rail Profile Detection System Based on Laser Profiler[J]. Electronic Science and Technology, 2020, 33(8): 28-33.

图/表 13

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

表1

表2

参考文献 20

[1]	曹响才. 钢轨轮廓测量仪的研制[D]. 南昌:南昌大学, 2013.
	Cao Xiangcai. Development of rail profile measuring instrument[D]. Nanchang:Nanchang University, 2013.
[2]	郭雁一夫. 钢轨轮廓检测系统的设计与实现[D]. 长沙:湖南大学, 2016.
	Guo Yanyifu. Design and implementation of rail profile detection system[D]. Changsha:Hunan University, 2016.
[3]	王世阳. 钢轨磨耗自动检测系统[D]. 成都:电子科技大学, 2011.
	Wang Shiyang. Rail wear automatic detection system[D]. Chengdu:University of Electronic Science and Technology of China, 2011.
[4]	高伟杰. 基于机器视觉的钢轨轮廓检测系统的研究[D]. 北京:北京交通大学, 2012.
	Gao Weijie. Study on the detection system of rail silhouette based on machine vision[D]. Beijing: Beijing Jiaotong University, 2012.
[5]	郑树彬, 柴晓冬, 安小雪, 等. 基于动态模板的钢轨磨耗测量方法研究[J]. 中国铁道科学, 2013(2):7-12.
	Zheng Shubin, Chai Xiaodong, An Xiaoxue, et al. Rail wear measuring method based on dynamic template[J]. China Railway Science, 2013(2):7-12.
[6]	Chongyi C, Chengguo W, Ying J. Study on numerical method to predict wheel/rail profile evolution to due to wear[J]. Wear, 2010,269(3):167-173. doi: 10.1016/j.wear.2009.12.031
[7]	占栋, 于龙, 肖建, 等. 钢轨轮廓全断面高精度动态视觉测量方法研究[J]. 铁道学报, 2015,37(9):96-106.
	Zhan Dong, Yu Long, Xiao Jian, et al. Study on high-accuracy vision measurement approach for dynamic inspection of full cross-sectional rail profile[J]. Journal of the China Railway Society, 2015,37(9):96-106.
[8]	王旺华, 毛庆州, 熊智敏. 钢轨几何尺寸自动测量系统研究[J]. 中国铁道科学, 2012,33(S1):118-121.
	Wang Wanghua, Mao Qingzhou, Xiong Zhimin. Research on the automatic measurement system for rail physical dimension[J]. China Railway Science, 2012,33(S1):118-121.
[9]	周清跃, 张银花, 田常海. 60N钢轨的廓型设计及试验研究[J]. 中国铁道科学, 2014,35(2):128-134.
	Zhou Qingyue, Zhang Yinhua, Tian Changhai, et al. Profile design and test study of 60N rail[J]. China Railway Science, 2014,35(2):128-134.
[10]	张瑛讳. 二维激光位移传感器的信息采集系统研究[D]. 长春:吉林大学, 2015.
	Zhang Yingwei. Research about the information acquisition system of 2D laser displacement sensor[D]. Changchun:Jilin University, 2015.
[11]	章敏. 基于激光三角法的物体三维轮廓测量系统[D]. 武汉:武汉理工大学, 2015.
	Zhang Min. The measurement system of objects' three-dimension figure based on the laser triangulation[D]. Wuhan:Wuhan University of Technology, 2015.
[12]	Valkenburg R J, Mcivor A M. Accurate 3-D measurement using a structured light system[J]. Image and Vision Computing, 1998,16(4):99-110. doi: 10.1016/S0262-8856(97)00053-X
[13]	徐胜. 基于面阵CCD的轨道磨耗测量与研究[D]. 成都:西南交通大学, 2013.
	Xu Sheng. Measurement and research of the rail wear based on plane array CCD[D]. Chengdu:Southwest Jiaotong University, 2013.
[14]	李雯. 基于激光轮廓传感器的钢轨外形检测系统[D]. 北京:中国铁道科学研究院, 2014.
	Li Wen. Rail shap inspect system based on laser profile sensors[D]. Beijing:China Academy of Railway Sciences, 2014.
[15]	He S, Li J, Gao X, et al. Application of FTP in flaw detection of rail web[J]. Optic-International Journal for Light and Electron Optics, 2015,126(2):187-190.
[16]	Feng D, Yan J, Huang P. A novel denoise method basedon the grey relational analysis[C]. Weihai:IEEE International Conference on Information Acquisition, 2006.
[17]	杨芳芳, 朱东升, 王志巍, 等. 利用灰色绝对关联分析的中值滤波方法研究[J]. 计算机工程与应用, 2013,49(13):160-164.
	Yang Fangfang, Zhu Dongsheng, Wang Zhiwei, et al. Study of median filter method using analysis of grey absolute relation[J]. Computer Engineering and Application, 2013,49(13):160-164.
[18]	Calors Ricolfe Viala, Antonio Jose Sanchez Salmeron. Robust metric calibration of non-linear camera lens distortion[J]. Pattern Recognition, 2010,43(4):1688-16699. doi: 10.1016/j.patcog.2009.10.003
[19]	朱元兰. 抽样中的极限误差与置信水平[J]. 中国统计, 2009(10):47-48.
	Zhu Yuanlan. Limit error and confidence level in sampling[J]. Chinese Statistics, 2009(10):47-48.
[20]	秦万广, 杨帆. 极限误差计算方法的研究[J]. 吉林师范大学学报(自然科学版), 2001(4):63-64.
	Qin Wanguang, Yang Fan. Study on the calculation method of limt error[J]. Jilin Normal University Journal(Natural Science Edition), 2001(4):63-64.

廓形参数	最大值	最小值	极限误差	标准差
钢轨高	176.068	176.017	0.051	0.014
轨头宽	70.569	70.511	0.058	0.016
轨底宽	150.001	149.951	0.050	0.014
轨腰宽	18.684	18.629	0.055	0.014

廓形参数	系统测量值			对比误差
廓形参数	最大值	最小值	平均值	对比误差
钢轨高	176.068	176.017	176.050	176.040	0.010
轨头宽	70.569	70.511	70.540	70.550	0.010
轨底宽	150.001	149.951	149.974	149.980	0.006
轨腰宽	18.684	18.629	18.664	18.650	0.014

基于激光轮廓仪的非接触式钢轨廓形检测系统

Non-contact Rail Profile Detection System Based on Laser Profiler

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 13

参考文献 20

相关文章 2

Metrics

本文评价

推荐阅读 10

[1]	吴一龙,万红进. 一种基于坐标变换的测角误差校正算法[J]. , 2016, 29(9): 45-.
[2]	刘江,魏静萱. 基于极坐标变换的改进NSGA-II算法[J]. , 2016, 29(2): 34-.