基于二维主分量分析的人耳身份识别研究

有效的特征提取方法是解决人耳身份识别任务的关键之一。以主分量分析（PCA）为代表的线性子空间方法在特征提取工作中得到了广泛应用。为了更有效地提取人耳图像特征并减少运算量,将基于二维图像矩阵的2D-PCA方法应用于人耳身份识别。针对三个USTB人耳图像库,采用最近邻分类器,研究了选用不同的特征维数、贡献率,及不同的相似性测度时,2D-PCA方法与传统的PCA方法的识别性能。交叉验证的实验结果表明：2D-PCA方法较PCA方法获得了更短的训练时间和更高的识别率,说明基于图像矩阵的2D—PCA方法是一种效率更高,鲁棒性更强的人耳身份识别方法。     

SWD6LTM410C船用柴油机拉缸故障的分析与排除

文章结合SWD6LTM410C船用柴油机,阐述了拉缸的现象、产生的机理及可能的原因,提出了故障排除的方法,并针对类似故障在柴油机日常维护管理中应注意的问题进行总结。     

考虑多维动态特征交互的高速公路实时事故风险建模

为探究天气和道路等特征,以及交通流、天气、道路及时间等多维动态特征之间的交互作用对实时事故风险预测模型精度的影响,本文基于京哈高速公路北京段的事故数据,以及匹配的交通传感器数据、天气数据和道路特征等,构建4个数据集,分别为只包含交通流变量,包含交通流变量、天气及时间特征变量,包含交通流变量、道路及时间特征变量,包含交通流变量、天气、道路及时间特征变量。从考虑多维动态特征的交互效应出发,基于深度交叉网络,提出一种新的实时事故风险预测模型。结果显示,本文所构建的深度交叉网络模型比其他几种实时事故风险预 测方法显示出更高的精度。模型的AUC值(Area Under Curve)可达0.8562,在0.2的概率阈值下, 可以正确分类84.26%的非事故数据和77.55%事故数据。结论表明,本文采用的多维动态特征交互样本条件下的深度交叉网络模型能够有效地预测高速公路交通事故,可为我国高速公路安全管理部门提供理论与技术支持。     

基于时间累计法的机车维修性预计分析

基于时间累积法,阐述机车维修性预计工作流程以及系统与LRU平均修复时间的计算方法。并以空调与通风系统为例,说明了系统及LRU平均修复时间的计算过程,并由此得出整车的平均修复时间。文章最后阐述了维修性预计对机车设计的指导作用以及使用时间累积法的局限性。     

Adhesion estimation at the wheel–rail interface using advanced model-based filtering

The railway industry in the UK is currently expanding the use of condition monitoring of railway vehicles. These systems can be used to improve maintenance procedures or could potentially be used to monitor current vehicle running conditions without the use of cost prohibitive sensors. This paper looks at a novel method for the online detection of areas of low adhesion in the wheel/rail contact that cause significant disruption to the running of a network, particularly in the autumn season. The proposed method uses a Kalman–Bucy filter to estimate the creep forces in the wheel–rail contact area; post-processing is then applied to provide information indicative of the actual adhesion level. The algorithm uses data that, in practice, would be available from a set of modest cost inertial sensors mounted on the vehicle bogie and wheel-sets. The efficacy of the approach is demonstrated using simulation data from a nonlinear dynamic model of the vehicle and its track interface.     

Fault classification method for the driving safety of electrified vehicles

A fault classification method is proposed which has been applied to an electric vehicle. Potential faults in the different subsystems that can affect the vehicle directional stability were collected in a failure mode and effect analysis. Similar driveline faults were grouped together if they resembled each other with respect to their influence on the vehicle dynamic behaviour. The faults were physically modelled in a simulation environment before they were induced in a detailed vehicle model under normal driving conditions. A special focus was placed on faults in the driveline of electric vehicles employing in-wheel motors of the permanent magnet type. Several failures caused by mechanical and other faults were analysed as well. The fault classification method consists of a controllability ranking developed according to the functional safety standard ISO 26262. The controllability of a fault was determined with three parameters covering the influence of the longitudinal, lateral and yaw motion of the vehicle. The simulation results were analysed and the faults were classified according to their controllability using the proposed method. It was shown that the controllability decreased specifically with increasing lateral acceleration and increasing speed. The results for the electric driveline faults show that this trend cannot be generalised for all the faults, as the controllability deteriorated for some faults during manoeuvres with low lateral acceleration and low speed. The proposed method is generic and can be applied to various other types of road vehicles and faults.     

基于多维信息特征分析的驾驶人认知负荷研究

准确评估驾驶人认知负荷水平，对于深入研究驾驶人行为特性，改善驾驶安全性具有重要意义。现有的驾驶人认知负荷分类方法，大多基于心电、脑电等生理信息和车辆信息，由于特征选择上的单一性，导致驾驶人认知负荷分类模型的分类精度不高。设计基于跟驰场景的不同认知负荷N-back次任务试验，通过采集受试者的生理信号和车辆信号，结合NASA_TLX主观评分和机器学习算法，提出了基于多维信息特征融合的驾驶人认知负荷分类方法。研究表明：基于生理信息和车辆信息的多维信息特征认知负荷分类方法，其精度显著高于传统的基于生理信息的认知负荷分类方法，以多维信息特征为输入，随机森林法以其稳定性好、抗过拟合能力强的特点，表现出优异的分类效果，相比神经网络和支持向量机，具有最高的平均分类精度。     

铁道车辆轴箱弹簧断裂故障对动力学性能的影响

车辆运行过程中,当轴箱弹簧突发断裂故障,会造成动力学状态和性能突变,一系悬挂刚度突然减小,暂时失去承载能力,会威胁车辆行车安全。结合整车的动力学仿真,建立了轴箱弹簧断裂过程的力学模型,对整个断裂过程进行仿真,模拟了轴箱弹簧突然断裂工况下车辆动力学性能变化,分析了轴箱弹簧断裂条件下车辆直线行车安全性以及曲线通过安全性。计算分析结果表明:轴箱弹簧突然断裂导致一系悬挂刚度剧变,引起轮轨垂向力先减小后增大,轮重减载率、脱轨系数等参数增大直至超限,但对轮轨横向力影响不大。     

丰田卡罗拉轿车转向困难故障分析

本文主要介绍一部2012款卡罗拉电动转向系统(EPS)的构造,工作原理,分析该车出现转向困难,组合仪表上P/S警告灯亮起故障的原因,并提出解决故障的方法。     

故障树分析与危险源日志的结合

主要介绍故障树分析方法,并结合工作经验讲述如何将故障树分析结果与危险源日志进行对应链接,从而清晰、形象的为危险源的来源提供有力证据,规范项目技术风险管理。