基于DSP和以太网的铁路道口监测系统

根据铁路道口安全管理的需要,本文设计开发了一套基于DSP和以太网技术的铁路道口监测系统,介绍了它的硬件组成和软件结构.该系统使得铁路道口监测网与信息网融为一体,实现了对列车速度、车厢数目和列车行进的方向等列车运行状态数据的实时采集、网络传输、动态显示、入库保存以及历史数据查询等功能,并且可以同时向道口站工作人员发出声光报警信号,实用结果证明了该方案的正确性.     

基于DSP和以太网的铁路道口监测系统

根据铁路道口安全管理的需要,本文设计开发了一套基于DSP和以太网技术的铁路道口监测系统,介绍了它的硬件组成和软件结构。该系统使得铁路道口监测网与信息网融为一体,实现了对列车速度、车厢数目和列车行进的方向等列车运行状态数据的实时采集、网络传输、动态显示、入库保存以及历史数据查询等功能,并且可以同时向道口站工作人员发出声光报警信号,实用结果证明了该方案的正确性。     

基于ZigBee技术的铁路道口预警系统设计

随着我国经济的快速发展,各种铁路道口之间的差别越来越大.这些情况各异的铁路道口严重威胁人身安全,为此设计了一套基于ZigBee和GPRS技术的铁路无人看守道口预警系统.该系统能实现列车接近检测并进行声光报警、列车到达撤销报警、预警系统故障自检测以及系统软件空中下载(over air download)自动更新等功能.为保证铁路行车安全提供了一个低成本、高可靠性的解决方案.     

大铁无人值守铁路道口自动控制系统的设计方案

大铁道口的交通运行安全一直是铁路运行安全的重中之重,当前高铁运行速度越来越快,这是对城市铁路道口交通安全的一次极大的考验,其安全问题日趋重要。笔者采用的方法为在铁路道口列车通行方向设置传感器,当列车通过该传感器时,系统会自动报警并发出警告,提示路过车辆和行人即将有列车通过,并放下路障,阻挡行人和通行车辆。当列车最后一节车厢通过铁路另一侧传感器时,系统自动解除警报并升起路障,放行车辆和行人,等待下一次列车通行。     

基于单片机和GPRS的矿区铁路道口监测系统

针对矿区铁路道口安全管理的需求,设计了一套基于Atmega128和GPRS的矿区铁路道口监测系统,并给出其总体结构框图.在介绍系统组成的基础上,对其硬件结构和软件实现流程做了详细阐述.实际运行证明该系统具有较强的可靠性和抗干扰性,能更好地监控矿区铁路道口的运行状态,从而为我国铁路运输业提供一个良好的解决方案.     

列车行车状态监测无线传感系统设计

为了满足列车及城轨安全运行的需求,根据地上(地下)的铁轨延伸地形特点,设计开发了一套列车行车状态无线监测系统;详细阐述了以CC2430为核心的传感器节点、以CC2430和MCP2515为核心的网关节点的软硬件设计和工作流程;实测结果表明,当安装在铁轨旁的相邻无线传感节点的距离不超过60m时,传输成功率可达96％以上,能够实时、可靠地监测铁轨压力、车辆轴承温度和声音参数的变化情形;系统完全不影响车辆的正常运行,是对现有铁路列车运行状态监控系统的有益补充.     

面向货运列车的物联网实时在线监控系统设计

针对铁路货车行车安全状态监测系统的应用需求,设计一种基于物联网技术的货运列车监控系统.系统采用ZigBee短距离无线通信技术搭建无线传感网络,通过各节车辆的传感器节点采集数据发送到机车监控平台,然后通过GPRS网络与地面监控中心进行通信,实现车地数据的实时传输.经测试,系统达到了良好的实时在线监测效果,在铁路货车行车安全监测领域具有广泛的应用前景.     

一种道口报警系统的设计

为了提高道口行车及行人的安全性,介绍了一种道口报警系统的设计方案,给出了该系统的总体结构、单片机选型、传感器检测电路、有源屏蔽驱动电路、信号处理电路及软件设计方法。该系统可在检测出行车信号后发出语音报警,并显示列车接近方向及速度,以提醒行人注意安全。测试结果表明,该系统可在列车距离道口1km内报警,报警时间为1min,报警准确率高,抗干扰能力强,性能稳定。     

基于单片机的铁路道口报警系统

本深题主要研究的是在单片机控制下实现铁路道口的安全报警系统。系统采用MC9S12XS128MAA单片机为平交道口自动报警系统的控制核心,通过安装存离道口不远处的铁路两旁的红外埘射传感器检测点,感应到车厢信息形成检测序列,经过信号处理用CAN总线通信协议进行数据传输,单片机接收到数据,经处理后控制道口交通灯以及语音报警模块,来提醒过路行人和车辆。同时道口栏术自动关闭确保过往行人及车辆的安全。列车驶离道口后,系统会自动打开栏木并停止报警,保持道口畅通。     

基于模糊神经网络的信号维护监测子系统故障预测研究

随着城市轨道交通信号系统功能逐步优化,信号系统已成为运营行车的控制大脑和安全防护的重要保障。信号系统设备故障对地铁线路列车准点、安全运行有较大影响。信号维护监测系统作为信号系统的设备状态监视终端,可实现对信号系统子系统状态监测和部分硬件设备故障状态预测,从而提示维护人员提前检修和更换设备。结合信号维护监测子系统(MSS)架构,以MSS采集的硬件故障作为研究对象,利用模糊神经网络自适应和模糊处理信息能力的优势,以故障发生时间、故障类型、既有故障发生频率、人员维护周期、同类型故障发生频次、设备使用时间等可用数值表征的参数作为输入变量,建立了基于模糊神经网络的信号设备故障预测模型。仿真结果表明,该预测模型能够准确跟随故障变化趋势、预测故障发生次数。基于模糊神经网络的预测方法可应用于轨道交通信号系统设备故障预测研究中。     

基于LabVIEW的高速列车远程监测系统设计

随着高铁运营速度的不断提高,对高速列车进行长期的实时监测,有利于保障列车的行车安全.在明确监测需求的基础上,开发出基于LabVIEW的高速列车车下设备舱远程监测系统.系统以LabVIEW软件为编程平台,以NI CompactRIO硬件结构体系为载体,选择可重复配置的FPGA数据采集模块进行数据采集,数据传输采用DMA FIFO方法,实现了车下设备舱关键受力部位吊架和支座数据的高速采集、实时显示、远程传输等功能.通过远程监测系统对采集的数据进行实时查看,可以实时掌握列车的运行状态,对维护列车运行的安全性和稳定性具有重要意义.     

Driver behaviour at rail level crossings: responses to flashing lights, traffic signals and stop signs in simulated rural driving

Australian road and railway authorities have made a concerted effort to reduce the number of rail level crossings, particularly the higher risk passive crossings that are protected by devices such as ‘give way’ or ‘stop’ signs. To improve this situation, passive level crossings are often upgraded with active controls such as flashing red lights. Traffic signals may provide good safety outcomes at level crossings but remain untested. The primary purpose of this research was to compare driver behaviour at two railway level crossings with active controls, flashing red lights and traffic signals, to behaviour at the current standard passive level crossing control, a stop sign. Participants drove the MUARC advanced driving simulator for 30 min. During the simulated drive, participants were exposed to three level crossing scenarios. Each scenario consisted of one of three level crossing control types, and was associated with an oncoming train. Mean vehicle speed on approach to the level crossings decreased more rapidly in response to flashing lights than to traffic signals. While speed on approach was lowest for the stop-sign condition, the number of non-compliant drivers (i.e., those who did not stop) at the crossing was highest for this condition. While results indicate that traffic signals at rail level crossings do not appear to offer any safety benefits over and above flashing red lights, further avenues of research are proposed to reach more definitive conclusions. Compliance was lowest for the passive crossing control which provides further support for the ongoing passive crossing upgrades in Australia.     

New methods for the condition monitoring of level crossings

Level crossings represent a high risk for railway systems. This paper demonstrates the potential to improve maintenance management through the use of intelligent condition monitoring coupled with reliability centred maintenance (RCM). RCM combines advanced electronics, control, computing and communication technologies to address the multiple objectives of cost effectiveness, improved quality, reliability and services. RCM collects digital and analogue signals utilising distributed transducers connected to either point-to-point or digital bus communication links. Assets in many industries use data logging capable of providing post-failure diagnostic support, but to date little use has been made of combined qualitative and quantitative fault detection techniques. The research takes the hydraulic railway level crossing barrier (LCB) system as a case study and develops a generic strategy for failure analysis, data acquisition and incipient fault detection. For each barrier the hydraulic characteristics, the motor's current and voltage, hydraulic pressure and the barrier's position are acquired. In order to acquire the data at a central point efficiently, without errors, a distributed single-cable Fieldbus is utilised. This allows the connection of all sensors through the project's proprietary communication nodes to a high-speed bus. The system developed in this paper for the condition monitoring described above detects faults by means of comparing what can be considered a ‘normal’ or ‘expected’ shape of a signal with respect to the actual shape observed as new data become available. ARIMA (autoregressive integrated moving average) models were employed for detecting faults. The statistical tests known as Jarque–Bera and Ljung–Box have been considered for testing the model.     

Decision support model for prioritizing railway level crossings for safety improvements: Application of the adaptive neuro-fuzzy system

Every year, more than 400 people are killed in over 1.200 accidents at road-rail level crossings in the European Union (European Railway Agency, 2011). Together with tunnels and specific road black spots, level crossings have been identified as being a particular weak point in road infrastructure, seriously jeopardizing road safety. In the case of railway transport, level crossings can represent as much as 29% of all fatalities caused by railway operations. In Serbia there are approximately 2.350 public railway level crossings (RLC) across the country, protected either passively (64%) or by active systems (25%). Passive crossings provide only a stationary sign warning of the possibility of trains crossing. Active systems, by contrast, activate automatic warning devices (i.e., flashing lights, bells, barriers, etc.) as a train approaches. Securing a level crossing (whether it has an active or passive system of protection) is a material expenditure, and having in mind that Serbian Railways is a public company directly financed from the budget of the Republic of Serbia, it cannot be expected that all unsecured level crossings be part of a programme of securing them. The most common choice of which level crossings to secure is based on media and society pressure, and on the possible consequences of a rise in the number of traffic accidents at the level crossings. The process of selecting a level crossing where safety equipment will be installed is accompanied by a greater or lesser degree of uncertainty of the essential criteria for making a relevant decision. In order to exploit these uncertainties and ambiguities, fuzzy logic is used in this paper. Here also, modeling of the Adaptive Neuro Fuzzy Inference System (ANFIS) is presented, which supports the process of selecting which level crossings should receive an investment of safety equipment. The ANFIS model is a trained set of data which is obtained using a method of fuzzy multi-criteria decision making and fuzzy clustering techniques. 20 experts in road and rail traffic safety at railway level crossings took part in the study. The ANFIS model was trained with the experiential knowledge of these experts and tested on a selection of rail crossings in the Belgrade area regarding an investment of safety equipment. The ANFIS model was tested on 88 level crossings and a comparison was made between the data set it produced and the data set obtained on the basis of predictions made by experts.     

基于无线传输的高速列车轴温集中监测系统

为避免高速列车在运行中产生热切轴现象,采用2.4GHz无线通信技术设计了轴温集中监测系统,系统由安装在车轴上的监测节点和监测台组成;监测节点采用ATmega128L作为微控制器,利用温度传感器PT100实现轴温的采集,并通过无线模块nRF24L01实现数据的传输;监测台主要负责集中接收、处理、显示和存储各监测节点发送来的数据;当轴温过高时,报警提醒驾驶员采取紧急措施,避免发生事故;经试验表明,该系统能够准确测量运行列车的轴温状态,给出了实验结果并进行了分析。     

基于移动IPv6的高速铁路通信快速切换方法

针对高速铁路移动通信系统中越区频繁、切换速度快、丢包延时等问题,根据高速铁路列车运行的自身特点,将移动IPv6切换技术与高速铁路移动通信网络相结合,在快速切换方案的基础上进行改进,提出了一种基于预测的高速铁路移动通信切换方法,为高速铁路安全高效运行提供了可靠优质的移动通信服务。实验结果表明,该方法缩减了移动检测、转交地址配置和重复地址检测的时间,提高了切换效率,解决了由于快速频繁切换而导致的网络资源利用率低和切换延迟大的问题。     

基于WSN的青藏铁路温度监测系统研究

无线传感器网络综合了传感技术、MEMS技术、无线通信技术和分布式信息处理技术等,能够通过各类集成化的微型传感器,实时、全方位地监测被测对象的信息,通过嵌入式系统对信息进行处理,并通过自组织无线通信网络,以多跳中继方式将所感知信息传送到终端。利用无线传感器网络可以很好地实现对青藏铁路冻土区的无人实时监测和无线数据传输,可以对冻土区铁路工程进行长期、全面、系统的观测,确保青藏铁路安全运营,并对铁路长期运营、维修和养护提供科学依据。