Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

地铁智能运维平台的环网架构及其性能分析

为实现地铁运维平台的智能化,需要将分散在各个专业系统、各个站点的"数据孤岛"打通,实现数据共享和数据融合。本文针对智能运维平台的应用,提出了3种不同的基于ERPS协议的环网架构,分析并计算其可靠性水平;通过实验室测试,对比每种环网架构的收敛性能指标,给出3种环网架构在地铁建设中的适用场合。     

基于材料就地再生的多功能养护车作业工艺及经济效益分析

多功能养护车是完成沥青路面病害快速处置的现代化作业机具,而将路面材料的就地再生利用作为养护车的标准配置是其作业功能的重要拓展,且是道路养护现代化的主要标志之一。探讨路面材料再生工艺及相关技术参数的优化对于保证和提高再生材料的路用质量、降低加热能源消耗和环境保护具有重要的现实意义。     

HXD3C机车主断路器异常断开故障分析与处理方法

在HXD3C机车运用阶段,连续多次出现机车主断路器无故断开问题,严重影响机车正常运行。为解决该问题,根据HXD3C机车主断路器工作原理及HXD3C机车微机控制中主断路器断开的逻辑关系,详细阐述了引起主断路器异常断开的条件,并结合HXD3C机车C5修检修期间及机务段运用过程中实际发生的故障案例,提出主断路器故障诊断方法和解决方案,完善主断路器故障诊断处理流程,并通过实际案例实施,取得了良好效果。     

城市道路路面养护措施探讨

现今国内大部分城市道路均存在不同程度的路面损坏情况，缩短了道路的使用时间。目前，不少城市已针对城市道路的现状进行相应的养护，并取得良好的效果，促进了城市的发展。     

浅谈非标工具在实际运用中的改造

文章结合多年机舱工作和车床操作实践经验，利用非标工具具有实用性、唯一性、创造性的特点，从注油工具设计改造、专用工具设计改造和设备零件工具改造3个方面探讨了对非标工具设计改造的做法，改造已成功运用到实际设备当中，为设备检修工作带来了便利，对相关工具的改造具有借鉴意义。     

沧州市新华桥检测及维修加固设计简介

简述沧州市新华桥试验检测过程及结论,针对桥梁缺损状况进行原因分析,详细介绍了维修加固设计要点及主要环节的施工工序。     

铝合金舰艇的腐蚀特点及治理对策

本文列举了铝合金腐蚀的典型类型，对目前铝合金舰艇上存在的主要腐蚀特点及原因进行了分析，提出制定维护保养制度，并从技术手段上提出了相应的治理防护对策。     

汽车电控发动机常见故障排除和维修

对电喷发动机各种油路、气路故障进行了讨论，提出相关故障排除及相应维修建议。     

河北省高速公路养护管理体系探析

通过借鉴国内、外高速公路的养护管理模式,对河北省高速公路管理体系进行剖析,提出符合河北省高速公路特点的养护管理模式.