Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

Xiukun Wei, Kun Guo, L. Jia, Guangwu Liu, Minzheng Yuan
{"title":"Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case","authors":"Xiukun Wei, Kun Guo, L. Jia, Guangwu Liu, Minzheng Yuan","doi":"10.4236/JILSA.2013.54029","DOIUrl":null,"url":null,"abstract":"This paper presents an innovative \napproach for the fault isolation of Light Rail Vehicle (LRV) suspension system \nbased on the Dempster-Shafer (D-S) evidence theory and its improvement \napplication case. The considered LRV has three rolling stocks and each one \nequips three sensors for monitoring the suspension system. A Kalman filter is \napplied to generate the residuals for fault diagnosis. For the purpose of fault \nisolation, a fault feature database is built in advance. The Eros and the norm \ndistance between the fault feature of the new occurred fault and the one in the \nfeature database are applied to measure the similarity of the feature which is \nthe basis for the basic belief assignment to the fault, respectively. After the basic belief \nassignments are obtained, they are fused by using the D-S evidence theory. The \nfusion of the basic belief assignments increases the isolation accuracy \nsignificantly. The efficiency of the proposed method is demonstrated by two case studies.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","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.2013.54029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

This paper presents an innovative approach for the fault isolation of Light Rail Vehicle (LRV) suspension system based on the Dempster-Shafer (D-S) evidence theory and its improvement application case. The considered LRV has three rolling stocks and each one equips three sensors for monitoring the suspension system. A Kalman filter is applied to generate the residuals for fault diagnosis. For the purpose of fault isolation, a fault feature database is built in advance. The Eros and the norm distance between the fault feature of the new occurred fault and the one in the feature database are applied to measure the similarity of the feature which is the basis for the basic belief assignment to the fault, respectively. After the basic belief assignments are obtained, they are fused by using the D-S evidence theory. The fusion of the basic belief assignments increases the isolation accuracy significantly. The efficiency of the proposed method is demonstrated by two case studies.
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D-S证据理论的轻轨车辆悬架系统故障隔离及改进应用案例
提出了一种基于Dempster-Shafer (D-S)证据理论的轻轨车辆悬架系统故障隔离创新方法及其改进应用实例。考虑的LRV有三个机车车辆,每个机车车辆配备三个传感器用于监测悬挂系统。利用卡尔曼滤波产生残差进行故障诊断。为了实现故障隔离,预先建立了故障特征库。应用Eros和新发生故障的故障特征与特征库中的故障特征之间的范数距离来度量特征的相似度,这是对故障进行基本信念赋值的基础。在得到基本信念赋值后,利用D-S证据理论对其进行融合。基本信念赋值的融合显著提高了分离精度。通过两个实例验证了该方法的有效性。
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