An experimental analysis on the turnover time of railway freight transportation based on the impact strength of factors

Based on the factors impact strength model(FISM), we studied on calculation formulas of influence strength and key elements of FISM, and analyzed the turnover time of railway freight transportation of China. The results show that wagon transfer time is the most critical factor among the three subjective factors of wagons turnover time. The FISM based analysis of wagon transfer time show that the wagon turnover time is significantly correlated with transit time with resorting. Among the seven factors of detention time of transit time with resorting, the time of waiting to departing, converging, and waiting to break-up are key factors, while the time of make-up, break-up, arrival and departure are general factors. We carried out one empirical research based on the data of Baoji East Railway Station in 2015. The results of empirical research and FISM are consistent completely.

Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images

There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.

Study on the Organization Model of Wagon Flows in Railway Terminal

Railway terminal is an important part of railway network. Transport organization of railway terminal is the key of the railway transport organization. Moreover, the organization of transport work is based on the organization of wagon flows in the railway terminal. Because of the great amounts of equipment and a large number of train operations, the study on railway terminal transport organization is mostly focused on a marshalling station in railway terminal or a part of it. Systematic study taking railway terminal as a whole is very few. In this paper, the organization of wagon flows in a railway terminal is analyzed and a wagon flow model in a railway terminal is established. The main principles of organization of local trains are also presented.

Research on Optimization of Selection of Train Passing Routes and Division of Shunting Work in Railway Hub

in this paper, a general integer prosramming model is set up, and its solution ispresented to optimize the organization of wagon flows and the determination ofthe train running route as well as division of shuntins work among stations in ahub. As application example is put forword, with some conclusions reached.

基于修程修制改革的铁路货车运用维修实践

铁路货车修程修制改革涉及定期检修和运用维修。介绍货车运用维修现状,从运用维修的人员组织、资源整合、作业效率、安全责任等方面入手,对存在的问题进行深入分析,查找原因,提出实施铁路货车运用维修改革,要创新思路,充分发挥新技术、新设备、新工艺的优势,落实安全质量保证,整合既有资源、强化优势互补,确立动态检、专项修、定期整的解决策略,为探索新形势下铁路货车修程修制改革提供了新的思路。

翻车机系统选型

翻车机是一种大型散货装卸车机械,与其他卸载敞车机械相比,具有卸载干净、卸载能力大、卸载效率高、自动化程度高等特点。文中对翻车机进行了分类总结。

发挥减速顶调速技术的优势解决对重载车辆制动能力不足的问题

为了满足编组站新型重载车辆对制动能力的新需要,需对减速顶调速设备进行改进,探索新的应用方式、研究新技术和新设备,把驼峰编组站调速技术推向新的高度。

基于Pearson系数与BP神经网络的编组站空车调配分类研究

为降低铁路空车调配的扣车率,在详细分析空重车流组织过程的基础上,提出在编组站对空车调配进行源头分类的对策。首先通过货运部门扣车检查项点筛选出厂修时长等评估指标,创建基于扣车标准的评价指标体系,其次利用Pearson相关性系数分析了评估指标的非线性特征,最后使用BP神经网络方法研究了空车扣车分类。结果表明:单隐层BP神经网络空车调配识别率达到0.8750;双隐层BP神经网络空车调配识别率达到0.8906。可见在编组站源头进行空车调配分类对减少货运、车辆、调度等部门的工作量,提升编组站空车调配区域内的车流组织效率都有较为积极的影响。