吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (6): 1974-1979.doi: 10.13229/j.cnki.jdxbgxb201506034

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面向高铁钢轨应力广域监测的无线传感网系统架构及性能测试

王立鼎1,2, 岳国栋1, 徐征1, 刘冲1, 陈义1, 赵悦璇1, 王天娆3   

  1. 1.大连理工大学 辽宁省微纳米技术及系统重点实验室,辽宁 大连 116085;
    2.吉林大学 机械科学与工程学院,长春 130022;
    3.苏州鼎汗传感网技术有限公司 技术中心,江苏 苏州 215011
  • 收稿日期:2014-04-21 出版日期:2015-11-01 发布日期:2015-11-01
  • 作者简介:王立鼎(1934-),男,教授,博士生导师,中国科学院院士.研究方向:物联网技术.E-mail:wangld@dlut.edu.cn
  • 基金资助:
    国家“十二五”科技支撑计划重点项目(2011BAG05B02); 中央高校基本科研业务费项目(DUT14LAB07)

Architecture and performance test of wireless sensor network system for distributed stress monitoring in high-speed railway track

WANG Li-ding1,2, YUE Guo-dong1, XU Zheng1, LIU Chong1, CHEN Yi1, ZHAO Yue-xuan1, WANG Tian-rao3   

  1. 1.Key Laboratory for Micro/Nano Technology and System of Liaoning Province, Dalian University of Technology, Dalian 116085, China;
    2.College of Mechanical Science and Engineering, Jilin University, Changchun 130022,China;
    3.Suzhou Ding-han Sensor Networks Technology Company Technology Center, Suzhou 215011, China
  • Received:2014-04-21 Online:2015-11-01 Published:2015-11-01

摘要: 建立了一种应用在高铁钢轨应力监测的无线传感网系统组成架构,它由数据采集层、通信及控制层、数据存储及分析层组成。提出了多传感节点信息的并行传输及处理方法,用于解决数据完整性和网络拓扑结构变化的即时响应等问题。利用关系型数据库和数据分析机构建数据存储和管理模式,管理及分析连续生成的数据。设计了“元信息捕获-本体映射-数据查询-数据获取-数据可视化”交互操作数据流,以实现设备分布和传感数据可视化。最后,在课题组架设于中国铁道科学研究院的钢轨应力监测无线传感网平台上,对建立的系统架构进行了性能测试,试验结果表明:系统架构应用在无线传感网上,丢包率小于5%,时延小于1 min,能够满足高铁钢轨应力分布式监测的需求。

关键词: 计算机应用, 无线传感网, 钢轨应力监测, 数据管理, 可视化

Abstract: A software architecture of wireless sensor network system, which consisted of data acquisition layer, control and communication layer and data storage and analysis layer, was established for distributed stress monitoring in high-speed railway track. A concurrent data processing method for the information from multi-sensor nodes was proposed to improve the key characteristics such as the data integrity and rapid response. The data storage and management model was constructed based on relational databases and data analysis machine to manage and analyze the continuously generated data. In addition, the data flow of the interactive manipulation for client software, called "Metadata capture-Ontology mapping-Data acquisition-Data visualization", was designed to display the equipment distribution and sensing data. Finally, the software architecture was implemented on the wireless sensor network platform at the China Academy of Railway Science. Experiment results show that the packet loss rate was less than 5% and transmission delay was less than 1 min. The performance of the WSN-based software architecture can satisfy the requirement of the distributed monitoring of high-speed rail stress.

Key words: computer application, wireless sensor network, railway track monitoring, data management, visualization

中图分类号: 

  • TP39
[1] Hada A,Soga K,Liu R S,et al. Lagrangian heuristic method for the wireless sensor network design problem in railway structural health monitoring[J]. Mechanical Systems and Signal Processing,2012,28:20-35.
[2] Yoon H J, Song K Y, Kim J S, et al. Longitudinal strain monitoring of rail using a distributed fiber sensor based on Brillouin optical correlation domain analysis[J]. NDT & E International,2011,44(7):637-644.
[3] Vuppala S K, Ghosh A, Patil K A, et al. A scalable wsn based data center monitoring solution with probabilistic event prediction[C]∥26th International Conference on Advanced Information Networking and Applications, Fukuoka,2012:446-453.
[4] Rakshit S M, Hempel M, Sharif H, et al. Hybrid technology networking: a novel wireless networking approach for real-time railcar status monitoring[DB/OL].[2014-04-24]. http://proceedings.asmedigitalcollection.asme.org/data/Conferences/ASMEP/75697/181_1.pdf.
[5] Costa B J A, Figueiras J A. Evaluation of a strain monitoring system for existing steel railway bridges[J]. Journal of Constructional Steel Research, 2012,72:179-191.
[6] Reason J M, Chen H, Crepaldi R, et al. Intelligent Telemetry for Freight Trains[M]. Berlin Heidelberg,Springer,2010.
[7] Shafiullah G M, Azad S A, Ali A B M S. Energy-efficient wireless MAC protocols for railway monitoring applications[J]. IEEE Transactions on Intelligent Transportation Systems,2013,14(2):649-659.
[8] Pylkkänen K, Luomala H, Guthrie W S, et al. Real-time in situ monitoring of frost depth, seasonal frost heave, and moisture in railway track structures[J]. Bridges,2014,10:446-455.
[9] Kerkez B, Glaser S D, Bales R C, et al. Design and performance of a wireless sensor network for catchment‐scale snow and soil moisture measurements[DB/OL]. [2014-04-24].http://onlinelibrary.wiley.com/doi/10.1029/2011WR011214/full.
[10] Niska S, Schunnesson H, Kumar U. Measurements and analysis of electromagnetic interference in a railway signal box—a case study[J]. International Journal of Reliability, Quality and Safety Engineering,2011,18(3):285-303.
[11] Schmidt D C, Huston S D. C++ Network Programming, Volume 1: Mastering Complexity with ACE and Patterns[M]. New Jersey: Addison Wesley, 2002.
[12] Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters[J]. Communications of the ACM, 2008,51(1):107-113.
[13] National Research Council. Understanding the Changing Planet: Strategic Directions for the Geographical Sciences[M]. Washington, DC: The National Academies Press,2010.
[14] 刘卉, 汪懋华, 王跃宣, 等. 基于无线传感器网络的农田土壤温湿度监测系统的设计与开发[J]. 吉林大学学报:工学版,2008,38(3):604-608.
Liu Hui, Wang Mao-hua, Wang Yue-xuan, et al. Development of farmland soil moisture and temperature monitoring system based on wireless sensor network[J]. Journal of Jilin University (Engineering and Technology Edition),2008,38(3):604-608.
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