2018 International Conference on Intelligent Rail Transportation (ICIRT)最新文献

Safer Rail Operations: Reactive to Proactive Maintenance Using State-of-the-Art Automated In-service Vehicle-Track Condition Monitoring 更安全的铁路运营:使用最先进的自动化在役车辆轨道状态监测对主动维护的反应

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641587

R. Ravitharan

{"title":"Safer Rail Operations: Reactive to Proactive Maintenance Using State-of-the-Art Automated In-service Vehicle-Track Condition Monitoring","authors":"R. Ravitharan","doi":"10.1109/ICIRT.2018.8641587","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641587","url":null,"abstract":"The railway operators are demanding higher productivity and throughput to meet increasing patronage and changing needs. These coupled with shrinking maintenance windows and tightening of available asset repair budgets necessitate responsive strategies to ensure continued safe operations, service reliability whilst at the same time looking at ways to create greater efficiencies.Increasingly, technology and system approaches are being utilised by leading railway organizations to overcome these challenges and to improve their operations. The Instrumented Revenue Vehicle (IRV) Technology and associated sophisticated automatic data processing system are key technological innovations providing significant benefit to these railway organizations. The IRV is an intelligent automated condition monitoring tool which is integrated into normal railway operations. IRV automatically collects dynamic vehicle performance data and identifies high risk track related defects, and the precise locations of the defects, capable of sending remote data that can be analysed in real time. This paper outlines the IRV technology, and how the shift from reactive to proactive maintenance and operation using new technological innovations, are providing significant benefits to the railway industry. Through the integration of the IRV intelligent, automated condition-monitoring tool, into normal everyday operations, the railway industry has an opportunity to create greater efficiencies, reduce maintenance costs, and to ensure safer operations, whilst at the same time extending asset life.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115635636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Use of thermal imaging technology for locomotive maintenance in Sri Lanka Railways 热成像技术在斯里兰卡铁路机车维修中的应用

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641623

Kuruppulage Asela Buddhika Pathirathna, Rathnayake Mudiyanselage Dhanushka, M. Rathnayake, Warnakulasooriya Hathanguruge, G. Fernando

{"title":"Use of thermal imaging technology for locomotive maintenance in Sri Lanka Railways","authors":"Kuruppulage Asela Buddhika Pathirathna, Rathnayake Mudiyanselage Dhanushka, M. Rathnayake, Warnakulasooriya Hathanguruge, G. Fernando","doi":"10.1109/ICIRT.2018.8641623","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641623","url":null,"abstract":"Predictive maintenance is one of the most important maintenance practice which can be used for locomotive maintenance purposes. Proper predictive maintenance procedures lead to reduce the downtime of locomotives, breakdown maintenance and train delays due to technical reasons. Predictive maintenance utilizes Non Destructive Testing (NDT) techniques such as Thermal imaging technology, Vibration analysis and Oil analysis. Sri Lanka Railway uses above technologies for locomotive maintenance purposes to a certain extent at present. From the beginning of 2016 the thermal imaging technology was introduced as a pilot project in one of the locomotive depot. During the last 24 months of the pilot project, it was able to identify several areas, where the thermal imaging technology can be successfully used for maintenance of locomotives. This paper tries to elaborate on the areas identified and how the thermal imaging technology is used as predictive maintenance practice in maintenance of locomotives in Sri Lanka Railways.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123125190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Self-supervised Railway Pantograph Image Component Retrieval with Geometry Prior 基于几何先验的自监督铁路受电弓图像分量检索

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641601

Peng Tang, Wei-dong Jin

引用次数: 0

Open Circuit Fault Diagnosis of Advanced Cophase Traction Power Supply System Based on Neural Network 基于神经网络的超前相牵引供电系统开路故障诊断

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641605

Xiaoqiong He, Haijun Ren, Pengcheng Han, Yang Chen, Zeliang Shu, Xiaoqiong He

{"title":"Open Circuit Fault Diagnosis of Advanced Cophase Traction Power Supply System Based on Neural Network","authors":"Xiaoqiong He, Haijun Ren, Pengcheng Han, Yang Chen, Zeliang Shu, Xiaoqiong He","doi":"10.1109/ICIRT.2018.8641605","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641605","url":null,"abstract":"Lots of power switch are used in advanced cophase traction power supply system, the open fault of power switch are bad for the reliability of the power supply system. Due to the variety of power switch and their non-linear characteristics, it is difficult to establish a mathematical model of the system to diagnose the open faults. This paper presents a fault diagnosis method based on Back propagation (BP) neural network. Firstly, the mechanism that the output level of the cascaded system will change when an open circuit fault occurs is analyzed. Then, According to the modulation strategy, the variation law of the harmonic of output voltage is analyzed, when the open-circuit fault occurs within the module or between modules. The feature quantities of all faults is extracted as training samples, with the trained three-layer neural network structure, the open circuit fault of the system can be diagnosed in real time. The simulation result shows that neural network fault diagnosis method can accurately and reliably diagnose the open fault of the power switch within 0. 02s without adding additional sensor.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124839053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Vehicle Scheduling with Multiple Trips and Time Windows and Long Planning Horizon 具有多行程、时间窗和长规划视界的车辆调度

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641584

Shudong Liu, Xiaoli Li, Shili Xiang

引用次数: 4

Research on Optimal Utilization Model and Algorithm of Urban Rail Transit Rolling Stock 城市轨道交通车辆优化利用模型与算法研究

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641620

Wenrong Wang, Y. Yue, Mingxin Li

引用次数: 0

Team Task Complexity of Traffic Dispatchers in Operating Control Center:A Network-based Concept 运营控制中心交通调度员团队任务复杂性:基于网络的概念

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641596

Ke Niu, Weining Fang, B. Guo

引用次数: 2

Constrained Consensus of Multiple Autonomous Vehicles with Nonconvex Constraints 非凸约束下多辆自动驾驶汽车的约束一致性

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641653

Mengmeng Duan, Hongqiu Zhu

引用次数: 1

Integrated Train Speed Profiles optimization Considering Signaling System and Delay 考虑信号系统和延迟的列车速度曲线综合优化

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641566

Chaoxian Wu, Shaofeng Lu, F. Xue, Lin Jiang

引用次数: 1

Improving Train Driving Performance under Disturbances by Intelligent Driver Advisory System 智能驾驶员咨询系统在干扰下提高列车行驶性能

2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641628

Hainan Zhu, Shigen Gao, Hai-rong Dong

引用次数: 0