Fault detection and isolation for a nonlinear railway vehicle suspension with a Hybrid Extended Kalman filter

Fault detection is considered to be one way to improve system reliability and dependability for railway vehicles. The secondary lateral and anti-yaw dampers are the most critical parts in railway suspension systems. So far, the dampers have been modelled as linear components in the fault detection and isolation observer design. In this work, a Hybrid Extended Kalman filter is used to capture the nonlinear characteristics of the dampers. In order to detect and isolate faults, a nonlinear residual generator is developed, which can distinguish clearly between different types of faults. A lateral half train model serves as an example for the proposed technique. The results show that failures in the nonlinear suspension system can be detected and isolated accurately.     

A real-time impact detection and diagnosis system of catenary using measured strains by fibre Bragg grating sensors

This paper describes an impact detection system using strain signals based on fibre optic sensors(FBG) for the real-time monitoring of the catenary system. The proposed detection system consists of three subsystems: a measuring system, a data processing and analysis system, and a status display and data access system. Because the strain signals obey the normal distribution, to monitor the catenary system in real time, a novel method that combines mobile standard deviation with the mobile Pauta criterion is proposed to distinguish real impact from the strain signal background. The use of this adaptive judging method reduces the misjudgment rate of impacts and improves the impact recognition accuracy. These impacts can be identified by the data analysis system, which provides impact location and their causes using the features of the catenary system. This method can simplify the detection system compared with the traditional location method. An application to a commercial metro line system indicated that the impacts on the catenary system were main caused by overlaps, expansion joints or steady arms, and were verified by correspondence with the floor plan of the catenary and manual inspection results. These results verified the reliability and effectiveness of the proposed impact detection system.     

铁道车辆热轴与红外线轴温探测角度探讨

介绍了客货车转向架上与红外线轴温探测相关的构件的外部形状和尺寸，论述了适合我国铁路列车提速、货车重栽的红外线轴温探测角度和实际运用结果。     

一种控制硬点受力状态的悬架可靠性试验方法研究

在悬架可靠性试验中,通过在其硬点位置安装力传感器,有效控制悬架硬点受力情况,使得悬架系统在台架试验中的受力状态更加真实和全面。该方法对于尚无白车身作为约束系统的悬架可靠性试验尤为有效。     

承唐高速公路箱梁冬季施工方案

介绍承唐高速公路箱梁施工项目的技术要点和施工方案,可为同类工程及业内同仁提供借鉴。     

广义相关时延估计方法在神经系统损伤检测中的应用

应用广义相关时间延迟估计方法，经由诱发电位潜伏期变化的测定，分析研究了撞击加速度和缺窒息实验条件下中枢时间系统的损伤问题。计算机模拟和诱发电位信号分析表明，当神经系统发生损伤时，诱发电位的潜伏期会出现显的变化，这种化可以由广义相关时间延迟估计方法而有效测定。     

公路工程试验检测方法的原理及应用

试验检测是公路工程中重要的管理方式,主要是对工程项目进行检测,是工程质量管理中的重要环节。文章介绍了公路工程试验检测方法的原理及应用情况。     

Development of a new time domain-based algorithm for train detection and axle counting

This paper presents an innovative train detection algorithm, able to perform the train localisation and, at the same time, to estimate its speed, the crossing times on a fixed point of the track and the axle number. The proposed solution uses the same approach to evaluate all these quantities, starting from the knowledge of generic track inputs directly measured on the track (for example, the vertical forces on the sleepers, the rail deformation and the rail stress). More particularly, all the inputs are processed through cross-correlation operations to extract the required information in terms of speed, crossing time instants and axle counter. This approach has the advantage to be simple and less invasive than the standard ones (it requires less equipment) and represents a more reliable and robust solution against numerical noise because it exploits the whole shape of the input signal and not only the peak values. A suitable and accurate multibody model of railway vehicle and flexible track has also been developed by the authors to test the algorithm when experimental data are not available and in general, under any operating conditions (fundamental to verify the algorithm accuracy and robustness). The railway vehicle chosen as benchmark is the Manchester Wagon, modelled in the Adams VI-Rail environment. The physical model of the flexible track has been implemented in the Matlab and Comsol Multiphysics environments. A simulation campaign has been performed to verify the performance and the robustness of the proposed algorithm, and the results are quite promising. The research has been carried out in cooperation with Ansaldo STS and ECM Spa.     

四次Bézier曲线形状调整方法

分析已有的带形状调节参数的扩展Bézier基生成的曲线的方法,提出带调节参数的控制点变换阵的调整曲线形状的方法.其原理是通过将原来的Bézier曲线的控制点变换为新的一组控制点,进而生成新的Bézier曲线.这组控制点的位置与参数相关联,几何意义明显.     

基于分段学习模型的自动驾驶行为决策算法研究

在具有车道线的特定自动驾驶场景中,针对目前端到端的行为决策算法直接输入原始图像进行决策导致的网络模型迁移性差、预测精度欠佳、泛化能力不足等问题,提出一种基于分段学习模型的车辆自动驾驶行为决策算法。首先,基于GoogLeNet建立一种端到端的车道线检测网络模型,并引入车道中心线作为决策的重要线索提高算法的迁移能力,同时利用YOLOv3目标检测模型对本车道内前方最近障碍物进行位置检测;而后,经几何测量模型将两者检测结果转换成环境状态信息向量为决策做支撑;最后,构建基于长短期记忆(LSTM)网络的驾驶行为决策模型,根据编码的历史状态信息刻画出动态环境中车辆的运动模式,并结合当前时刻的状态推理得到驾驶行为参量。使用建立的真实驾驶场景数据集对模型分别进行训练、验证与测试,离线测试结果显示车道线检测模型的检测位置误差小于1.3%,车道内前方障碍物检测模型的检测精度达98%以上,驾驶行为决策网络模型表征预测优度的决定系数 大于0.7。为进一步验证算法的有效性,搭建了Simulink/PreScan联合仿真平台,多种工况下的仿真验证试验中多个评价指标均达到工程精度要求,实车测试的试验结果也表明该算法可实现复杂驾驶场景下平稳、准确无偏航的预测效果并满足实时性要求,且与传统端到端模式的算法相比,具有更好的迁移性和泛化能力。