考虑区域协调性的城际列车开行方案优化

针对城际列车开行方案没有有效匹配城市轨道交通运能的问题，提出一种考虑区域协调性的城际列车开行方案优化方法。首先，以旅客出行费用最小和铁路运输效益最大为优化目标，考虑城际列车载客能力、出发地目的地（Original Destination，OD）客流需求和通过能力等约束；然后，在此基础上增加运能匹配度的限制，构建了考虑区域协调性的城际列车开行方案多目标非线性规划模型，并设计改进的模拟退火算法求解模型；最后，以广深城际铁路为例并进行两组对比分析。实验结果表明:考虑区域协调性的列车开行方案可以使旅客出行总广义费用降低约4.06%，铁路部门的效益提高约9.58%，旅客和铁路的系统总成本降低约23.27%；与遗传算法相比，改进的模拟退火算法在求解质量与收敛速度上均有较大提高。所提模型和算法可充分兼顾旅客和铁路双方利益，能够为城际列车开行方案优化问题提供有效解决方法。     

多运营商高铁隧道移动网络覆盖方案

高铁移动网络覆盖是国内三大通信运营商的一个重点,而高铁隧道内移动网络覆盖更是运营商的一大难点痛点。文章根据我国中部省份某高铁线路覆盖规划实例,采用“设备+POI+泄漏电缆”模式,即三家运营商信号源设备通过同一POI(point of interface,多系统接入平台)接入,信号输出到泄漏电缆进行隧道覆盖,隧道口场坪站安装宽频切换天线对隧道外进行延伸覆盖,通过链路预算合理布置各运营商主设备信号源,从而实现隧道到室外的无缝覆盖。最后,根据已有成熟网络覆盖解决方案,对未来5G高铁隧道移动网络覆盖方案进行了探讨。     

列车贮藏方式对生猪肉原料新鲜度的影响研究

探究夏季高温期某铁路局集团公司旅客列车贮藏条件下生猪肉原料的品质变化,为改善餐车生猪肉保存及取用方式、保障旅客列车食品安全提供可靠依据。在实验室条件下模拟该列车餐车实际条件下生猪肉贮藏及取用方式:将生猪肉分别在4℃(冷藏)和-10℃(冷冻)两种条件下贮藏,再解冻,使用,再贮藏。通过猪肉的色泽、气味、组织状态感官指标以及挥发性盐基氮(TVB-N)、硫化氢、球蛋白沉淀理化指标的变化为依据,来评价猪肉的新鲜度。结果表明:夏季高温期间,该铁路局集团公司列车生猪肉原料,在4℃冷藏贮存条件下,有2d保质期,在-10℃冷冻贮藏条件下,10d内猪肉品质能够保持新鲜,满足铁路运输的时限要求。该铁路公司列车贮藏和使用生猪肉符合规范,用肉时限在保质期范围内,能够保证全年时段肉质新鲜安全。为保障铁路旅客的食品安全,生猪肉原料应贮存在制冷效果良好的冷冻冰箱内,取用肉类置于冷藏保存的,应在2d内使用完毕。     

2K-H型人字齿周转轮系的免组装设计与成型

为了解决人字齿周转轮系加工、装配的难题,提出利用3D打印技术制造免组装人字齿周转轮系的新工艺。通过理论推导,建立变位系数、齿侧间隙与齿轮基本参数之间的关系。利用建立的关系式,对行星轮进行负变位,设计具有微量齿侧间隙的2K-H型人字齿周转轮系。利用熔融沉积成型实验平台使设计的人字齿周转轮系成型,成型后的人字齿周转轮系齿轮啮合传动正常,设计、制作相应辅件,将其装配成减速步进电机。结果表明,通过合适的间隙设计,可以利用3D打印技术实现人字齿周转轮系的免组装制造,为小尺寸、一体式人字齿周转轮系个性化定制提供了一种具有工业应用前景的全新解决方案。     

车轮多边形磨耗统计规律及关键影响因素分析

针对列车车轮多边形磨耗问题广泛存在于轨道交通运输领域,会导致车辆/轨道系统产生高频的振动冲击,严重影响车辆和轨道系统零部件的使用寿命,危及行车安全这一问题,调查了大量车轮的多边形磨耗情况并进行统计分析,掌握了高速列车车轮多边形磨耗问题的现状和特点。以18～20阶多边形磨耗车辆为例,通过理论研究和试验分析(试验分析包括车辆系统振动特性测试和转向架模态特性测试),对车轮多边形磨耗的根本原因及诱导因素进行研究。研究发现,轮轨系统在580 Hz频率附近存在固有模态是导致车辆发生18～20阶多边形磨耗的根本原因,轮轨表面的各种不平顺能激发或者加剧轮轨系统在580 Hz频率附近的模态共振,从而诱发车轮多边形磨耗的产生。该结果可为高速列车车轮多边形磨耗问题的防止和进一步研究提供参考。     

Position computation models for high-speed train based on support vector machine approach

High-accuracy positioning is not only an essential issue for efficient running of high-speed train (HST), but also an important guarantee for the safe operation of high-speed train. Positioning error is zero when the train is passing through a balise. However, positioning error between adjacent balises is going up as the train is moving away from the previous balise. Although average speed method (ASM) is commonly used to compute the position of train in engineering, its positioning error is somewhat large by analyzing the field data. In this paper, we firstly establish a mathematical model for computing position of HST after analyzing wireless message from the train control system. Then, we propose three position computation models based on least square method (LSM), support vector machine (SVM) and least square support vector machine (LSSVM). Finally, the proposed models are trained and tested by the field data collected in Wuhan-Guangzhou high-speed railway. The results show that: (1) compared with ASM, the three models proposed are capable of reducing positioning error; (2) compared with ASM, the percentage error of LSM model is reduced by 50.2% in training and 53.9% in testing; (3) compared with LSM model, the percentage error of SVM model is further reduced by 38.8% in training and 14.3% in testing; (4) although LSSVM model performs almost the same with SVM model, LSSVM model has advantages over SVM model in terms of running time. We also put forward some online learning methods to update the parameters in the three models and better positioning accuracy is obtained. With the three position computation models we proposed, we can improve the positioning accuracy for HST and potentially reduce the number of balises to achieve the same positioning accuracy.     

基于深度信念网络的列车故障音频诊断方法

铁路在交通运输行业有着举足轻重的地位，一旦列车发生故障将会导致严重的生命财产损失。由于列车发生故障的概率相对较低，因此难以捕获列车的故障样本。针对上述问题，提出了一种无监督学习的列车故障识别方法，通过检测列车音频信号来识别列车故障。该方法基于深度信念网络(DBN)，利用小波包分解提取检测信号的特征向量并将其作为DBN的输入，待网络充分训练后，由训练好的DBN识别当前列车的运行状况。现场监测实验结果表明，该方法能够在无监督的条件下有效识别列车故障，保障了列车的运行安全。     

Influences of chip serration on micro-topography of machined surface in high-speed cutting

Saw-tooth chip changes from macroscopically continuous ribbon to separated segments with the increase of cutting speed. The aim of this study is to find the correlations between chip morphology and machined surface micro-topography at different chip serration stages encountered in high speed cutting. High strength alloy steel AerMet100 was employed in orthogonal cutting experiments to obtain chips at different serration stages and corresponding machined surfaces. The chips and machined surfaces obtained were then examined with optical microscope (OM), scanning electron microscope (SEM), and white light interferometer (WLI). The result shows that chip serration causes micro-waves on machined surface, which increases machined surface roughness. However, wave amplitudes (surface roughness) at different serration stages are different. The principal factor influencing wave amplitude is the thickness of the sawed segment (tooth) of saw-tooth chip. With cutting parameters in this study, surface roughness contributed by chip serration ranges from 0.39 μm to 1.85 μm. This may bring on serious problems in the case of trying to replace grinding with high-speed cutting in rough machining. Some suggestions have been proposed to control the chip serration-caused surface roughness in high-speed cutting based on the results of the current study.     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.