浅谈青藏线转辙设备的养护与维修

本文根据CTS2轨枕式转辙机本身的特点以及青藏铁路的管理模式，从养护、维修的角度，简要说明了CTS2轨枕式转辙机需要的养护工作量、故障处理方法、维修特点及在施工中要注意的问题。     

基于蠕滑机理的重载货车车轮磨耗研究

在多体动力学分析软件包SIMPACK中建立重载货车动力学模型,基于轮轨蠕滑机理在MATLAB软件中编制车轮踏面磨耗仿真程序WWS.根据车轮磨耗仿真结果和现场实测结果对Zobory磨耗模型进行修正.研究车辆非理想状态对车轮磨耗的影响,分析轮轨型面和转向架结构对车辆非理想状态的适应性.通过钢轨表面滚动接触疲劳损伤特征的研究,对车轮滚动接触疲劳和磨耗耦合关系进行数值模拟.主要研究内容和结论如下.     

基于激光位移传感器的车轮踏面磨耗检测方法研究

针对当前城轨车辆车轮踏面磨耗人工检测劳动强度高、检测精度低的问题,提出一种基于激光位移传感器的车轮踏面磨耗检测方法。首先在轨道外侧安装一组激光位移传感器进行车轮踏面数据采集;其次结合标准轮对踏面轮廓数据,采用数据预处理、坐标旋转、数据融合等算法获取实际车轮踏面轮廓线;最后根据踏面磨耗几何关系获得车轮踏面磨耗值。通过踏面磨耗检测误差分析以及现场标准轮对实验和过车实验表明,所提方法检测精度为±0.2 mm,抗干扰能力强,能够满足踏面磨耗检测实际要求。     

ZYJ7型电动液压转辙机控制电路故障判断与处理

根据ZYJ7型电动液压转辙机启动、表示电路原理,结合日常处理故障经验,从室内到室外阐述了判断ZYJ7型电动液压转辙机启动电路、表示电路各种故障方法,为我们日常处理ZYJ7型电动液压转辙机启动电路、表示电路故障提供了准确、快捷的处理方法。     

荧光磁粉探伤裂纹智能识别图像处理研究

磁粉探伤的发展趋势是自动化、智能化,而工件表面状况、真伪裂纹、工况条件等使得现有的检测识别方法难以满足工件表面裂纹缺陷自动检测识别的需要。在分析工件表面荧光磁粉图像特征及裂纹缺陷特征的基础上,研究表征裂纹邻域像素空间相关度的二维直方图分布,提出基于多重分块极值的图像边缘检测算法。根据裂纹邻域像素空间相关度参数,以及裂纹缺陷的长宽比、圆形度等特征,设计了基于Fisher线性判别方法的工件裂纹识别算法。以此为基础的荧光磁粉探伤工件裂纹缺陷自动检测识别技术,应用于列车轮轴检测线实时检测,裂纹缺陷的有效检出率达99%。     

汽车车轮侧滑量检测存在问题及对策研究

在分析侧滑产生机理的基础上，论述了目前侧滑检测中存在的检测结果离散性大、判定失准等问题。指出产生这些问题的原因除车速、载荷、胎压等因素外，还包括侧滑台的技术状况、检测站的设计形式等因素。针对问题提出了稳定检测条件、合理制定标准、提高检测技术等实现侧滑量准确检测与正确评定的有效措施。     

智能车辆机器视觉发展近况

简要回顾了机器视觉的早期发展，从简单的特定场合应用到模拟人眼的复杂系统设计，介绍了机器视觉产品在智能汽车上的应用情况，分析了世界各国在车用机器视觉技术领域研究开发情况以及未来发展趋势。     

基于视觉的前方车辆探测技术研究方法综述

对国内外关于车道上本车前方车辆检测方法进行了综述。根据探测车辆所采用的传感器不同,分别对采用单目视觉、双目视觉、视觉与其他传感器融合以及非视觉传感器的几种探测方法进行了比较与分析。同时,针对每种探测方法,进一步阐述了目前车辆识别的一些具体算法。最后,指出了每种方法存在的优点和一些不足。     

Wheel-rail contact models for vehicle system dynamics including multi-point contact

Advanced modelling of rail vehicle dynamics requires realistic solutions of contact problems for wheels and rails that are able to describe contact singularities, encountered for wheels and rails. The basic singularities demonstrate themselves as double and multiple contact patches. The solutions of the contact problems have to be known practically in each step of the numerical integration of the differential equations of the model. The existing fast, approximate methods of solution to achieve this goal have been outlined. One way to do this is to replace a multi-point contact by a set of ellipses. The other methods are based on so-called virtual penetration. They allow calculating the non-elliptical, multiple contact patches and creep forces online, during integration of the model. This allows nearly real-time simulations. The methods are valid and applicable for so-called quasi-Hertzian cases, when the contact conditions do not deviate much from the assumptions of the Hertz theory. It is believed that it is worthwhile to use them in other cases too.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.