Data-driven optimization of railway maintenance for track geometry

Railway big data technologies are transforming the existing track inspection and maintenance policy deployed for railroads in North America. This paper develops a data-driven condition-based policy for the inspection and maintenance of track geometry. Both preventive maintenance and spot corrective maintenance are taken into account in the investigation of a 33-month inspection dataset that contains a variety of geometry measurements for every foot of track. First, this study separates the data based on the time interval of the inspection run, calculates the aggregate track quality index (TQI) for each track section, and predicts the track spot geo-defect occurrence probability using random forests. Then, a Markov chain is built to model aggregated track deterioration, and the spot geo-defects are modeled by a Bernoulli process. Finally, a Markov decision process (MDP) is developed for track maintenance decision making, and it is optimized by using a value iteration algorithm. Compared with the existing maintenance policy using Markov chain Monte Carlo (MCMC) simulation, the maintenance policy developed in this paper results in an approximately 10% savings in the total maintenance costs for every 1 mile of track.     

南京长江大桥的轨道几何状态变化规律的分析

位于京沪线上的南京长江大桥系该线贯通大江南北的主要咽喉.由于其轨道结构的差异,相应的轨道几何状态变化是不一样的.本文采用不平顺幅值和功率谱相对比的方法,综合分析大桥在重载快速行车条件下的轨道几何状态变化规律,为合理制定相应的养修标准提供了技术支持.     

Design of propeller geometry using streamline-adapted blade sections

We refine the geometry of a propeller by modifying the blade sections to align them with surface streamlines, obtained by the panel method. Redefinition of the blade sections aligned with the streamlines is provided together with surface modeling scheme by which model propellers were built. Numerical simulations and open-water tests on models suggest a possible increase of 1% in propeller efficiency.     

Position synchronization for track geometry inspection data via big-data fusion and incremental learning

Track geometry inspection data is important for managing railway infrastructure integrity and operational safety. In order to use track geometry inspection data, having accurate and reliable position information is a prerequisite. Due to various issues identified in this research, the positions of different track geometry inspections need to be aligned and synchronized to the same location before being used for track degradation modeling and maintenance planning. This is referred to as “position synchronization”, a long-standing important research problem in the area of track data analytics. With the aim of advancing the state of the art in research on this subject, we propose a novel approach to more accurately and expediently synchronize track geometry inspection positions via big-data fusion and incremental learning algorithms. Distinguishing it from other relevant studies in the literature, our proposed approach can simultaneously address data exceptions, channel offsets and local position offsets between any two inspections. To solve the Position Synchronization Model (PS-Model), an Incremental Learning Algorithm (IL-Algorithm) is developed to handle the “lack of memory” challenge for the fast computation of massive data. A case study is developed based on a dataset with data size of 18 GB, including 58 inspections between February 2014 and July 2016 over 323 km (200 miles) of tracks belonging to China High Speed Railways. The results show that our proposed model performs robustly against data exceptions via the use of multi-channel information fusion. Also, the position synchronization error using our proposed approach is within 0.15 meters (0.5 feet). Our proposed data-driven, incremental learning algorithm can quickly solve the complex, data-extensive, position synchronization problem, using an average of 0.1 s for processing one additional kilometer of track. In general, the data analysis methodology and algorithm presented in this paper are also suitable to address other relevant position synchronization problems in transportation engineering, especially when the dataset contains multiple channels of sensors and abnormal data outliers.     

2轮／轨几何学（待续）——轨道车辆与线路的共同课题

对车轮和轨道的一般性测量早就做出过定义，但两者的生产制造精度存在很大的差别。只有掌握了车轮和轨道的轮廓型面，并将两者相结合，才可能掌握几何接触状况对铁路运营车辆的运行性能所产生的影响。为了保证运营的安全、经济和舒适，应当在较长周期内对轮／轨系统确定新的条件，因此，需要独立的车辆和线路监测系统。     

传统弯桥微分方程求解中一个理论问题

在假定平曲线梁的平面内及平面外弯曲符合直梁的物理关系,平曲梁的扭矩与扭率的关系与直梁相同的条件下,根据微分几何理论,推导平曲线梁桥微单元轴向应变、弯曲曲率增量、扭转曲率与梁段单元位移、扭转关系的几何微分方程,构建曲梁的微分物理方程。理论分析结果表明:微分方程中曲梁的竖向弯曲曲率增量仅与曲梁的竖向变形有关,与梁体扭率无关;曲梁的扭率与曲梁的竖向变形无关。该方程与相关文献中推导和引用的曲梁微分物理方程(基本假定相同)结论不同。其原因是,推导过程中没有采用传统的几何方法,而是采用微分几何建立各变量之间的联系。     

轮轨两点接触的简易计算方法

本文给出轮轨两点接触判断和计算的简化方法,编制了电子计算机程序。实例计算表明,该法简便直观、计算速度快,精度可满足机车车辆动力学研究的需要。     

Construction of a stochastic model of track geometry irregularities and validation through experimental measurements of dynamic loading

This paper describes the construction of a stochastic model of urban railway track geometry irregularities, based on experimental data. The considered irregularities are track gauge, superelevation, horizontal and vertical curvatures. They are modelled as random fields whose statistical properties are extracted from a large set of on-track measurements of the geometry of an urban railway network. About 300–1000 terms are used in the Karhunen–Loève/Polynomial Chaos expansions to represent the random fields with appropriate accuracy. The construction of the random fields is then validated by comparing on-track measurements of the contact forces and numerical dynamics simulations for different operational conditions (train velocity and car load) and horizontal layouts (alignment, curve). The dynamics simulations are performed both with and without randomly generated geometrical irregularities for the track. The power spectrum densities obtained from the dynamics simulations with the model of geometrical irregularities compare extremely well with those obtained from the experimental contact forces. Without irregularities, the spectrum is 10–50?dB too low.     

基于电控集成智能控制的柴油机降油耗技术研究

为了进一步降低商用车的燃油消耗和CO2排放,开展基于发动机工况需求的电控集成优化控制研究。以某直列4缸电控共轨柴油机为研究对象,以发动机水温为反馈,对发动机水泵、节温器等电控冷却系统零部件进行综合调节,实现快速暖机升温以及正常行驶中发动机水温恒定在最佳温度,并通过优化VGT,改善燃烧过程。试验结果表明,采用智能控制系统可将冷却水温控制在各工况所对应的最优冷却水温附近,基于发动机工况需求的电控集成优化控制可以降低柴油机油耗3.51%,充分验证了智能控制方案的可行性和有效性。     

车辆装载几何参数超限识别方法

为了提高超限运输检测的精度,提出了车辆装载几何参数超限识别方法。应用2个激光测距传感器分别扫描通过车辆,得到不完整的端部轮廓以及各点坐标值,根据左轮廓最小横坐标、右轮廓最大横坐标与两轮廓的最小纵坐标,计算了车辆装载宽度和高度,再经过轮廓合成,得到完整的正投影,应用光幕和光电开关构成的复合基准点判定装载长度,并与载货汽车运输超限判定标准比较。现场测试结果表明:检测误差在±3%的范围内,可以实时存储通过车辆身份,因此,识别方法精度较高,检测结果直观。