Analysis on Space-time Characteristics and Reliability Evaluation of Train Control On-board Subsystem

被引:0
|
作者
Chen B. [1 ]
Cai B. [1 ,2 ,3 ]
Shangguan W. [1 ,2 ,3 ]
Wang J. [1 ,2 ,3 ]
机构
[1] School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing
[2] State Key Laboratory of Rail Traffic Control and Safety, Beijing
[3] Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing
来源
Tiedao Xuebao/Journal of the China Railway Society | 2019年 / 41卷 / 12期
关键词
Failure characteristics; Reliability; Space-time characteristics; Train control system; Weibull distribution;
D O I
10.3969/j.issn.1001-8360.2019.12.008
中图分类号
学科分类号
摘要
This paper introduced the model of data space aiming at the features of complex and chaotic data of train control on-board system(TC-OBS). From the perspectives of physical space and time, the spatial characteristic data model and the time characteristic data model of TC-OBS were established respectively. On this basis, a system reliability analysis algorithm based on space-time characteristics was designed to analyze the reliability of typical systems and equipment. The results show that in terms of physical space characteristics, a small number of trains have higher fault rates and the proportion of failures of four types of faults such as communication accounts for about 74% of all faults. In respect of the time characteristics, the fault rate of system group fluctuates widely. The monthly failure rate of a single system is about 10%. The number of failures has no clear dependency on operation time. The interval time between failures of selected two types of equipment is in accord with the Weibull distribution. The time for the reliability of communication equipment to drop to 50% is about 200 hours, while the time for the reliability of the relay equipment to drop to 50% is about 100 hours, which means that the reliability of communication equipment is higher than that of the relay equipment. Practice has proved that the data space model and space-time characteristics analysis proposed in this paper can better support the reliability analysis of on-board system. © 2019, Department of Journal of the China Railway Society. All right reserved.
引用
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页码:56 / 65
页数:9
相关论文
共 22 条
  • [1] Cao Y., Wang G., Bao Z., Et al., Temporal and Spatial Evolvement Model of Power Grid, Electric Power Automation Equipment, 29, 1, pp. 1-5, (2009)
  • [2] Han A., Zhang Z., Yin X., Et al., Research on Fault Characteristic and Grid Connecting-point Protection Scheme for Wind Power Generation with Doubly-fed Induction Generator, Transactions of China, Electrotechnical Society, 27, 4, pp. 233-239, (2012)
  • [3] Yang L., Bao L., Mining Spatiotemporal Co-occurrence Pattern in Equipment Failure, Ordnance Industry Automation, 35, 6, pp. 46-51, (2016)
  • [4] Matsukawa T., Funakoshi H., Analyzing Failure Frequency and Severity in Communication Networks, Reliability and Maintainability Symposium (RAMS), 2010 Proceedings-Annual, pp. 1-6, (2010)
  • [5] Jager G., Zug S., Casimiro A., Generic Sensor Failure Modeling for Cooperative Systems, Sensors, 18, (2018)
  • [6] Han Z., Zhu J.K., Zou J., Et al., Characteristics of Commutation Failure Based on Fault Recording, The Journal of Engineering, 2019, 16, pp. 1346-1349, (2019)
  • [7] Wang F., Xu T., Tang T., Et al., Bilevel Feature Extraction-based Text Mining for Fault Diagnosis of Railway Systems, IEEE Transactions on Intelligent Transportation Systems, 18, 1, pp. 49-58, (2017)
  • [8] Zhao Y., Xu T., Wang H., Text Mining Based Fault Diagnosis of Vehicle On-board Equipment for High Speed Railway, 2014 IEEE 17th International Conference On Intelligent Transportation Systems (ITSC), pp. 900-905, (2014)
  • [9] Zang Y., Shangguan W., Zhang J., Et al., Application of Hidden Markov Model for Fault Analysis of ODO in Train Location Unit, Journal of Southwest Jiaotong University, 52, 6, pp. 1-8, (2017)
  • [10] Yin J., Zhao W., Fault Diagnosis Network Design for Vehicle On-board Equipments of High-speed Railway: a Deep Learning Approach, Engineering Applications of Artificial Intelligence, 56, pp. 250-259, (2016)