Study on the Real-Time Security Evaluation for the Train Service Status Using Safety Region Estimation

Guiling Liao, Yong Qin, Xiaoqing Cheng, Lisha Pan, Lin He, Shan Yu, Yuan Zhang
{"title":"Study on the Real-Time Security Evaluation for the Train Service Status Using Safety Region Estimation","authors":"Guiling Liao, Yong Qin, Xiaoqing Cheng, Lisha Pan, Lin He, Shan Yu, Yuan Zhang","doi":"10.4236/JILSA.2013.54025","DOIUrl":null,"url":null,"abstract":"For the important issues of security service of rail vehicles, the online quantitative security assessment method of the service status of rail vehicles and the key equipments is urgently needed, so the method based on safety region was proposed in the paper. At first, the formal description and definition of the safety region were given for railway engineering practice. And for the research objects which their models were known, the safety region estimation method of system stability analysis based on Lyapunov exponent was proposed; and for the research objects which their models were unknown, the data-driven safety region estimation method was presented. The safety region boundary equations of different objects can be obtained by these two different approaches. At last, by real-time analysis of the location relationship and generalized distance between the equipment service status point and safety region boundary, the online safety assessment model of key equipments can be established. This method can provide a theoretical basis for online safety evaluation of trains operation; furthermore, it can provide support for real-time monitoring, early warning and systematic maintenance of rail vehicles based on the idea of active security.a","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"221-226"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"智能学习系统与应用(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/JILSA.2013.54025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

For the important issues of security service of rail vehicles, the online quantitative security assessment method of the service status of rail vehicles and the key equipments is urgently needed, so the method based on safety region was proposed in the paper. At first, the formal description and definition of the safety region were given for railway engineering practice. And for the research objects which their models were known, the safety region estimation method of system stability analysis based on Lyapunov exponent was proposed; and for the research objects which their models were unknown, the data-driven safety region estimation method was presented. The safety region boundary equations of different objects can be obtained by these two different approaches. At last, by real-time analysis of the location relationship and generalized distance between the equipment service status point and safety region boundary, the online safety assessment model of key equipments can be established. This method can provide a theoretical basis for online safety evaluation of trains operation; furthermore, it can provide support for real-time monitoring, early warning and systematic maintenance of rail vehicles based on the idea of active security.a
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于安全区域估计的列车服务状态实时安全评估研究
针对轨道车辆安全服务的重要问题,迫切需要对轨道车辆及关键设备的服务状态进行在线定量安全评估方法,本文提出了基于安全区域的方法。首先,针对铁路工程实际,给出了安全区域的形式化描述和定义。针对模型已知的研究对象,提出了基于Lyapunov指数的系统稳定性分析安全区域估计方法;针对模型未知的研究对象,提出了数据驱动的安全区域估计方法。这两种方法可以得到不同目标的安全区域边界方程。最后,通过实时分析设备使用状态点与安全区域边界之间的位置关系和广义距离,建立关键设备在线安全评估模型。该方法可为列车运行在线安全评价提供理论依据;基于主动安全思想,为轨道车辆的实时监控、预警和系统维护提供支持
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
135
期刊最新文献
Architecting the Metaverse: Blockchain and the Financial and Legal Regulatory Challenges of Virtual Real Estate A Proposed Meta-Reality Immersive Development Pipeline: Generative AI Models and Extended Reality (XR) Content for the Metaverse A Comparison of PPO, TD3 and SAC Reinforcement Algorithms for Quadruped Walking Gait Generation Multiple Collaborative Service Model and System Construction Based on Industrial Competitive Intelligence Skin Cancer Classification Using Transfer Learning by VGG16 Architecture (Case Study on Kaggle Dataset)
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1