Application of BP Neural Network in Fault Diagnosis of Railway Turnout Control Circuit

被引：0
|
作者：
Liu, Ming-Ming [1 ,2 ]
He, Hu [2 ]
Dong, Wei [1 ]
Sun, Xin-Ya [1 ]
Wang, Shi-Liang
机构：
[1] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Beijing, Peoples R China
[2] Syst Engn Res Inst Beijing Engn Equipment, Beijing, Peoples R China
来源：
2015 INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND INFORMATION SYSTEM (SEIS 2015) | 2015年
关键词：
High Speed Railway Turnout; Control Circuit; FCM; BP Neural Network;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
High speed railway turnout is an important equipment which is directly contacted with the high speed train. However, it is still in a simple way to deal with the faults of the control circuit by simple instruments and artificial experience. In order to realize the intelligent diagnosis of the fault of the turnout control circuit, this paper combines the principle and fault characteristics of the turnout control circuit. Based on the fuzzy C means algorithm, a typical sample of the fault is constructed. Based on the analysis of the detection and diagnosis of the fuzzy similarity relation, 11 typical failure modes and the corresponding 8 fault characteristics are obtained. Finally, based on the BP neural network, the intelligent diagnosis of the faults of the turnout control circuit is accomplished. The BP neural network model is constructed by MATLAB, and it is found that the diagnostic results can meet the expected requirements of the diagnosis. This method will have good application prospect in this field.
引用
收藏
页码：503 / 509
页数：7
相关论文
共 50 条
  • [1] The Railway Turnout Fault Diagnosis Algorithm Based on BP Neural Network
    Zhang, Kai
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON CONTROL SCIENCE AND SYSTEMS ENGINEERING, 2014, : 135 - 138
  • [2] Algorithm of Railway Turnout Fault Detection Based on BP Neural Network
    Zhang, Kai
    Du, Kai
    Ju, Yong-feng
    [J]. INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND ENGINEERING (ACSE 2014), 2014, : 114 - 119
  • [3] Fault Diagnosis method for Railway Turnout Control Circuit based on Information Fusion
    Liu, Mingming
    Yan, Xiang
    Sun, Xinya
    Dong, Wei
    Ji, Yindong
    [J]. 2016 IEEE INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC), 2016, : 315 - 320
  • [4] Fault Diagnosis of High-Speed Railway Turnout Based on Convolutional Neural Network
    Zhang, Peng
    Zhang, Guohua
    Dong, Wei
    Sun, Xinya
    Ji, Xingquan
    [J]. 2018 24TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC' 18), 2018, : 719 - 724
  • [5] The Fault Diagnosis of Digital Circuit on the Basis of the Rough BP Neural Network
    Zhouxun, Wangxiaoli
    [J]. PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2012, : 2297 - 2299
  • [6] Fault Diagnosis of Analog Circuit Based on PSO and BP Neural Network
    JI Mengran
    CHEN Gang
    YANG Qing
    ZHANG Jinge
    [J]. 沈阳理工大学学报, 2014, 33 (05) : 90 - 94
  • [7] Application of BP Neural Network in Fault Diagnosis of FOG SINS
    Wu, Lei
    Sun, Feng
    Chen, Shitong
    [J]. 2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 9322 - 9326
  • [8] The application of improved BP neural network in the engine fault diagnosis
    Lu Di
    Wang Jie
    [J]. PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE, 2012, : 3352 - 3355
  • [9] The Application of LMD and BP Neural Network in Gear Fault Diagnosis
    Li, Zhirong
    Wan, Zhou
    Xiong, Xin
    Liao, Xingzhi
    [J]. 2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2013, : 4609 - 4612
  • [10] Algorithm of Speed-up Turnout Fault Intelligent Diagnosis Based on BP Neural Network
    Zhang, Kai
    Ju, Yongfeng
    Du, Kai
    Bao, Xu
    [J]. PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION, 2017, 53 : 283 - 292
12345