Joint Fault Diagnosis Method of Multiclass Faults for Traction Rectifier in High-speed Train

被引：0
|
作者：
Tao H.-W. [1 ,2 ]
Peng T. [1 ,2 ]
Yang C. [1 ,2 ]
Chen Z.-W. [1 ,2 ]
Gui W.-H. [1 ]
机构：
[1] School of Automation, Central South University, Changsha
[2] Hunan Provincial Key Laboratory of Energy Saving Control and Satety Monitoring of Rail Transit, Central South University, Changsha
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 12期
基金：
中国国家自然科学基金;
关键词：
High-speed train; Joint diagnosis; Open-circuit fault; Sensor fault; Traction rectifier;
D O I：
10.16383/j.aas.c190258
中图分类号：
学科分类号：
摘要：
A joint fault diagnosis method of multiclass faults for the traction rectifier in high-speed train is proposed. First of all, based on open-circuit fault analysis of three-level traction rectifier, the state space models of rectifier operating in the normal condition and abnormal conditions that open-circuit fault happened in all power devices are built respectively. The corresponding state observers are established as well. Next the fault is detected by the normal state observer, open-circuit faults in power devices and grid side current sensor faults are distinguished by the fault state observers. Then the open-circuit fault location of power devices and fault type of grid side current sensor are obtained. The feasibility and effectiveness of the proposed method are verified by the real-time simulation results. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：2294 / 2302
页数：8
相关论文
共 50 条
  • [31] Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices
    Wu, Yunkai
    Jiang, Bin
    Lu, Ningyun
    Yang, Hao
    Zhou, Yang
    [J]. ISA TRANSACTIONS, 2017, 67 : 183 - 192
  • [32] Open-circuit fault diagnosis of single-phase three-level rectifier in high-speed train traction system based on adaptive sliding mode observer
    Xu S.-Q.
    Wang J.
    Hu Y.-Q.
    Zhang K.
    Jiang P.
    Chai Y.
    [J]. Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2023, 40 (11): : 1920 - 1930
  • [33] Ensefgram: An optimal demodulation band selection method for the early fault diagnosis of high-speed train bearings
    Wang, Cuiping
    Qi, Hongyuan
    Hou, Dongming
    Han, Defu
    Yang, Jiangtian
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2024, 213
  • [34] AttGGCN Model: A Novel Multi-Sensor Fault Diagnosis Method for High-Speed Train Bogie
    Man, Jie
    Dong, Honghui
    Jia, Limin
    Qin, Yong
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (10) : 19511 - 19522
  • [35] Convolutional Recurrent Neural Network for Fault Diagnosis of High-Speed Train Bogie
    Liang, Kaiwei
    Qin, Na
    Huang, Deqing
    Fu, Yuanzhe
    [J]. COMPLEXITY, 2018,
  • [36] Fault Diagnosis of High-Speed Train Bogie Based on Synchrony Group Convolutions
    Wu, Yunpu
    Jin, Weidong
    Ren, Junxiao
    Sun, Zhang
    [J]. SHOCK AND VIBRATION, 2019, 2019
  • [37] Significance Support Vector Machine for High-Speed Train Bearing Fault Diagnosis
    Sun, Bing
    Liu, Xiaofeng
    [J]. IEEE SENSORS JOURNAL, 2023, 23 (05) : 4638 - 4646
  • [38] Fault Diagnosis of High-speed Train Bogie Based on Deep Neural Network
    Zhang, Yuanjie
    Qin, Na
    Huang, Deqing
    Liang, Kaiwei
    [J]. IFAC PAPERSONLINE, 2019, 52 (24): : 135 - 139
  • [39] Fault diagnosis of high-speed train bogie based on LSTM neural network
    Deqing Huang
    Yuanzhe Fu
    Na Qin
    Shibin Gao
    [J]. Science China Information Sciences, 2021, 64
  • [40] Fault diagnosis of high-speed train bogie based on LSTM neural network
    Deqing HUANG
    Yuanzhe FU
    Na QIN
    Shibin GAO
    [J]. Science China(Information Sciences), 2021, 64 (01) : 260 - 262
12345