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

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Fault Diagnosis on Train Brake System Based on Multi-dimensional Feature Fusion and GBDT Enhanced Classification 基于多维特征融合和GBDT增强分类的列车制动系统故障诊断
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641607
Meng Zhang, Zhen Liu, X. Dang
{"title":"Fault Diagnosis on Train Brake System Based on Multi-dimensional Feature Fusion and GBDT Enhanced Classification","authors":"Meng Zhang, Zhen Liu, X. Dang","doi":"10.1109/ICIRT.2018.8641607","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641607","url":null,"abstract":"The condition of train brake system directly affects the performance and safety of the train. In view of the low accuracy and inefficiency of train brake system fault diagnosis, a new fault diagnosis method for train brake system based on multi-dimension feature fusion and GBDT enhanced classification is proposed. Firstly, the initial features of the brake system data are extracted from four dimensions, including time domain, frequency domain, wavelet packet decomposition and correlation. Secondly, features, which have great contributions to the fault diagnosis model, are screened out by the ReliefF algorithm, and the uncorrelated components are eliminated with the KPCA algorithm. So the core feature vectors can be obtained with the help of feature selection and reduction. Finally, a gradient boosting decision tree (GBDT) model for fault diagnosis is trained by the core feature vectors. And the model will be used to identify and diagnose the faults of the brake system. Experimental results show that, the fault diagnosis model proposed in this paper can identify the common fault types of brake system, and has high model train efficiency and excellent fault recognition performance.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"10 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":"116178226","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: 10
TrackNet - A Deep Learning Based Fault Detection for Railway Track Inspection 基于深度学习的铁路轨道检测故障检测方法
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641608
Ashish James, Wang Jie, Yang Xulei, Chenghao Ye, Nguyen Bao Ngan, Lou Yuxin, Su Yi, V. Chandrasekhar, Zeng Zeng
{"title":"TrackNet - A Deep Learning Based Fault Detection for Railway Track Inspection","authors":"Ashish James, Wang Jie, Yang Xulei, Chenghao Ye, Nguyen Bao Ngan, Lou Yuxin, Su Yi, V. Chandrasekhar, Zeng Zeng","doi":"10.1109/ICIRT.2018.8641608","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641608","url":null,"abstract":"Reliable and economical inspection of rail tracks is paramount to ensure the safe and timely operation of the railway network. Automated vision based track inspection utilizing computer vision and pattern recognition techniques have been regarded recently as the most attractive technique for track surface defect detection due to its low-cost, high-speed, and appealing performance. However, the different modes of failures along with the immense range of image variations that can potentially trigger false alarms makes the vision based track inspection a very challenging task. In this paper, a multiphase deep learning based technique which initially performs segmentation, followed by cropping of the segmented image on the region of interest which is then fed to a binary image classifier to identify the true and false alarms is proposed. It is shown that the proposed approach results in improved detection performance by mitigating the false alarm rate.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"1 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":"121412648","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: 28
Reallocating siding tracks in a railway station under severe disruptions 在铁路严重中断的情况下，重新分配铁路侧线
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641521
Rui Wang, Y. Bao, Weining Hao
{"title":"Reallocating siding tracks in a railway station under severe disruptions","authors":"Rui Wang, Y. Bao, Weining Hao","doi":"10.1109/ICIRT.2018.8641521","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641521","url":null,"abstract":"The arrangement of siding tracks in a station plays an important part in the station operation plan, especially when some trains are delayed. Due to speed limitation command by the dispatcher, trains are usually delayed getting into a station. When it occurs, the train may not reach its original siding track at its scheduled timetable and will have to be reallocated at a new time point, either to its original siding track or another. Then, the train‘s practical arrival time will further cause conflicts with other trains for occupying the same resources. Therefore, this paper focuses on the problem of station siding tracks reallocation after a delay, of which the first issue is to decide the siding tracks reallocated to delayed trains; the second one is to reschedule the arrival and departure time of trains passing through the station. A mixed-integer linear model is proposed to tackle the issue and real-world experiments based on Shijiazhuang (SJZ) high-speed railway passenger station are carried out to demonstrate the validity and efficiency of the model.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"31 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":"122839474","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: 1
An AI based High-speed Railway Automatic Train Operation System Analysis and Design 基于人工智能的高速铁路列车自动运行系统分析与设计
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641650
Miao Zhang, Qi Zhang, Yisheng Lv, Wenzhe Sun, Haiteng Wang
{"title":"An AI based High-speed Railway Automatic Train Operation System Analysis and Design","authors":"Miao Zhang, Qi Zhang, Yisheng Lv, Wenzhe Sun, Haiteng Wang","doi":"10.1109/ICIRT.2018.8641650","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641650","url":null,"abstract":"Recent years, the research and application of High-Speed Railway (HSR) automatic train operation (ATO) system are under fast development, while the safety, energy efficiency and passenger comfort of ATO systems still need improvement. On the other hand, Artificial Intelligence (AI) technology, for example, Deep Learning, has been widely applied in automata industry such as robot control and driverless vehicle. In this paper, we propose a new idea of improving train control system performance with AI technologies such as Deep Reinforcement Learning and Imitation learning, and describe the system objective, structure and development process. The details of key processes such as establishment of Train Running Condition Evaluation Index, acquisition and processing of relevant big data, construction of AI based automatic train operation model and the program of simulation and experiment are presented in this paper, which provides a brand new and practical idea to the development of High-Speed Railway automatic train operation systems.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"73 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":"122927017","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: 9
Data Analysis for Anomaly Detection to Secure Rail Network 铁路网络异常检测的数据分析
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641555
Huaqun Guo, Xiaoyi Shen, W. Goh, Luying Zhou
{"title":"Data Analysis for Anomaly Detection to Secure Rail Network","authors":"Huaqun Guo, Xiaoyi Shen, W. Goh, Luying Zhou","doi":"10.1109/ICIRT.2018.8641555","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641555","url":null,"abstract":"The security, safety and reliability of rail systems are of the utmost importance. In order to better detect and prevent anomalies, it is necessary to accurately study and analyze the network traffic and abnormal behaviors, as well as to detect and alert any anomalies if happened. This paper focuses on data analysis for anomaly detection with Wireshark and packet analysis system. An alert function is also developed to provide an alert when abnormality happens. Rail network traffic data have been captured and analyzed so that their network features are obtained and used to detect the abnormality. To improve efficiency, a packet analysis system is introduced to receive the network flow and analyze data automatically. The provision of two detection methods, i.e., the Wireshark detection and the packet analysis system together with the alert function will facilitate the timely detection of abnormality and triggering of alert in the rail network.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"6 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":"116952640","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: 1
Detection Probability of a Very High Throughput MAC Protocol for Satellite Intelligent Train Tracking 一种用于卫星智能列车跟踪的高吞吐量MAC协议的检测概率
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641626
D. Wong, Xiaoming Peng, F. Chin
{"title":"Detection Probability of a Very High Throughput MAC Protocol for Satellite Intelligent Train Tracking","authors":"D. Wong, Xiaoming Peng, F. Chin","doi":"10.1109/ICIRT.2018.8641626","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641626","url":null,"abstract":"This paper analyzes the detection probability and throughput of a Very High Throughput (VHT) Medium Access Control (MAC) protocol for an Intelligent Train Tracking System via a Low Earth Orbit (LEO) satellite. The key contributions of this paper are the proposed MAC protocol and its analytical formulation of the throughput and the detection probability of trains at a LEO satellite. The throughput of the VHT LEO MAC protocol is modeled by a discrete-time Markov Chain. One of the key advantages is that the maximum throughput gain (MTG) of the VHT MAC protocol is about 171% over that of the Slotted Aloha (SA) MAC protocol. The other advantage is that the detection probability of the trains using the VHT LEO MAC protocol is much better than that of the SA MAC protocol. These advantages add on to the added reliability, robustness and resilient for an Intelligent Train Tracking System using a LEO satellite or a constellation of LEO satellites.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"81 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":"134041092","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: 1
Assessment of Metro Signalling System Resilience 地铁信号系统弹性评估
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641658
Q. Wei, R. Niu, T. Tang
{"title":"Assessment of Metro Signalling System Resilience","authors":"Q. Wei, R. Niu, T. Tang","doi":"10.1109/ICIRT.2018.8641658","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641658","url":null,"abstract":"There is high failure rate in signalling system, any signalling failure may have influence on system capacity. The concept of resilience is introduced to reflect the change of system capacity in case of failure and defined as the ability of system to maintain capacity in degraded mode and recover from disruptions. In this work, in order to quantify the signalling system resilience, three system performance indexes are formulated and calculated by the train tracking model. By comparison, the train tracking interval is selected as the performance index. With the index, resilience in different disruption scenarios are measured, the results show system resilience is closely related to fault types. In addition, when the primary mode of signalling system fails, the system resilience varies greatly with different backup modes, it is proved that optimize the design of backup modes is helpful to improve the system resilience.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"58 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":"133129980","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: 0
A Method for Calculating Capacity of A Medium-Speed Maglev Line 一种中速磁浮线路容量计算方法
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641664
Qunyan Wang, L. Meng, Qingying Lai, Jun Liu, Yazhi Xu
{"title":"A Method for Calculating Capacity of A Medium-Speed Maglev Line","authors":"Qunyan Wang, L. Meng, Qingying Lai, Jun Liu, Yazhi Xu","doi":"10.1109/ICIRT.2018.8641664","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641664","url":null,"abstract":"The purpose of the analysis of capacity usage is to utilize the medium-speed maglev infrastructure in a more efficient and practical way. In this paper, the power supply zone is modeled by 'operation resource' which ensures the different safety headway of successive trains depending on the running speed. Then, by the analysis of virtual cell, a capacity calculating model is built to solve the problem of medium-speed maglev line capacity. And a Lagrangian relaxation algorithm is designed to decrease the difficulty of solving the model by relaxing the capacity constraint. Finally, numerical examples are given to illustrate the correctness of the proposed methods.","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":"121019045","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: 0
Microstructure and Wear Performance Assessment of Laser Cladded Rail Steel for Service Life Extension at Sharp-Radius Curves 激光包覆轨道钢在锐半径曲线下的组织与磨损性能评价
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641613
A. S. Nellian, K. E. Tan, H. Hoh, J. Pang, Ivan Christian, Si Yun Chua
{"title":"Microstructure and Wear Performance Assessment of Laser Cladded Rail Steel for Service Life Extension at Sharp-Radius Curves","authors":"A. S. Nellian, K. E. Tan, H. Hoh, J. Pang, Ivan Christian, Si Yun Chua","doi":"10.1109/ICIRT.2018.8641613","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641613","url":null,"abstract":"Excessive rail wear causes significant reduction in service life and increases the possibility of derailment. Sharp curves in particular, are more susceptible to wear than straights. Hence, the curved sections are manufactured from head hardened rail steel (R350HT grade) instead of the standard R260 rail steel due to their greater wear resistance. With the emergence of advanced rail systems, failures due to wear are still predominant, resulting in frequent maintenance or replacement of rails which is not cost effective. Hence, the prospect of repair via laser cladding has been studied to improve wear performance for extended service life and minimize financial burden. In this paper, the clad material’s compatibility with R350HT grade rail steel is evaluated. The transition in microstructure across the interfacial zone from the clad to the substrate is presented along with the hardness profile. Laboratory based ball-on disc wear tests serve as a preliminary indicator of the wear performance of the laser cladded R350HT grade rail steel. It is evident from the study that laser cladding of R350HT grade rails is feasible with capability to enhance service life at sharp-radius curves when adopted strategically.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"17 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":"129510285","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: 1
Knowledge-Graph Based Multi-Target Deep-Learning Models for Train Anomaly Detection 基于知识图的列车异常检测多目标深度学习模型
2018 International Conference on Intelligent Rail Transportation (ICIRT) Pub Date : 2018-12-01 DOI: 10.1109/ICIRT.2018.8641687
Zhiliang Qin, Chen Cen, Wang Jie, Teo Sin Gee, V. Chandrasekhar, Zhongbo Peng, Zeng Zeng
{"title":"Knowledge-Graph Based Multi-Target Deep-Learning Models for Train Anomaly Detection","authors":"Zhiliang Qin, Chen Cen, Wang Jie, Teo Sin Gee, V. Chandrasekhar, Zhongbo Peng, Zeng Zeng","doi":"10.1109/ICIRT.2018.8641687","DOIUrl":"https://doi.org/10.1109/ICIRT.2018.8641687","url":null,"abstract":"The state-of-art image segmentation algorithms can be applied to accurately localize objects by using deep convolutional neural networks (CNN). In this paper, we consider the anomaly detection problem encountered in a train wheel system. We propose a progressive approach to use a multi-target network to segment each component of the considered system sequentially by decoupling the segmentation and the classification task. Moreover, we use the knowledge graph approach to establish a semantic consistency matrix by quantifying the spatial relationship between various components. We show that by establishing a knowledge graph of the normally operating systems, we are able to identify a faulty component effectively.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"28 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":"127004508","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
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