Volume 15 Issue 1
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2015  >  15(1): 58-65
Next Previous
LI Yi-fan, LIU Jian-xin, LIN Jian-hui, LI Zhong-ji. Fault diagnosis method of railway vehicle with wheel flat based on self-adaptive multi-scale morphology analysis[J]. Journal of Traffic and Transportation Engineering, 2015, 15(1): 58-65. doi: 10.19818/j.cnki.1671-1637.2015.01.008
Citation: LI Yi-fan, LIU Jian-xin, LIN Jian-hui, LI Zhong-ji. Fault diagnosis method of railway vehicle with wheel flat based on self-adaptive multi-scale morphology analysis[J]. Journal of Traffic and Transportation Engineering, 2015, 15(1): 58-65. doi: 10.19818/j.cnki.1671-1637.2015.01.008
shu

Fault diagnosis method of railway vehicle with wheel flat based on self-adaptive multi-scale morphology analysis

doi: 10.19818/j.cnki.1671-1637.2015.01.008
  • 1.

    E'mei Campus Department of Mechanical Engineering, Southwest Jiaotong University, E'mei 614202, Sichuan, China

  • 2.

    State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

  • 3.

    Institute of Science and Technology, China Railway Eryuan Engineering GroupCo., Ltd., Chengdu 610031, Sichuan, China

More Information
  • Author Bio:

    LI Yifan(1985-), male, lecturer, PhD, + 86-833-5198322, Liyifan@swjtu.edu.cn

  • Received Date: 2014-07-08
  • Publish Date: 2015-02-25
    Abstract
  • A vehicle system dynamics model with 56 degrees of freedom and a wheel flat model were set up to calculate railway vehicle dynamic responses.The vibration information of vehicle was often influenced by various interferences, such as track irregularity and vehicle speed alteration.In order to effectively extract the wheel-track impact features from strong background noises, a self-adaptive multi-scale morphology filtering analysis algorithm was proposed to study the axle box vibration characteristics caused by wheel flat.The influences of track irregularity and vehicle running speed on the fault diagnosis result of axle box were discussed.Simulation result shows that the fault frequencies of 10, 15, 20 Hz are obtained by using morphology filter based on 7-scale and 9-scale structural elements at the speeds of 100, 150, 200km·h-1 with the American fifth grade and third grade track irregularities.Test result demonstrates that the fault frequency of 2 Hz is obtained by using morphology filter based on 7-scale structural element atthe speed of 40km·h-1, which is corresponding to the theoretic frequency of wheel flat, so diagnosis result is reliable.

     

    • FullText(HTML)
  • loading
    • References(19)
  • [1]
    TIAN Li-li, FANG Zong-de, ZHAO Yong. Location methods of peeling and flat spots in detection of wheel tread damages of railway wagons[J]. Journal of the China Railway Society, 2009, 31(5): 31-36. (in Chinese) doi: 10.3969/j.issn.1001-8360.2009.05.005
    [2]
    BRIZUELA J, FRITSCH C, IBáÑEZ A. Railway wheel-flat detection and measurement by ultrasound[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(6): 975-984. doi: 10.1016/j.trc.2011.04.004
    [3]
    FENG Qi-bo, ZHAO Yan, CUI Jian-ying. New dynamic method to quantitatively measure wheel flats of trains[J]. Chinese Journal of Mechanical Engineering, 2002, 38(2): 120-122. doi: 10.3321/j.issn:0577-6686.2002.02.028
    [4]
    MILKOVIC' D, SIMIC' G, JAKOVLJEVIC' n. Wayside system for wheel-rail contact forces measurements[J]. Measurement, 2013, 46(9): 3308-3318. doi: 10.1016/j.measurement.2013.06.017
    [5]
    FILOGRANO M L, GUILLéN P C, RODRíGUEZ-BARRIOS A, et al. Real-time monitoring of railway traffic using fiber bragg grating sensors[J]. IEEE Sensors Journal, 2012, 12(1): 85-92. doi: 10.1109/JSEN.2011.2135848
    [6]
    BELOTTI V, CRENNA F, MICHELINI R C, et al. Wheel-flat diagnostic tool via wavelet transform[J]. Mechanical Systems and Signal Processing, 2006, 20(8): 1953-1966. doi: 10.1016/j.ymssp.2005.12.012
    [7]
    GE Lin-fu, YIN Zhi-ben, ZHU Huai-fang. Micro-computer intelligence detection of locomotive wheel tread irregularities[J]. Journal of the China Railway Society, 1993, 15(2): 35-39. doi: 10.3321/j.issn:1001-8360.1993.02.005
    [8]
    GULLERS P, DREIK P, NIELSEN J C O, et al. Track condition analyser: identification of rail rolling surface defects, likely to generate fatigue damage in wheels, using instrumented wheelset measurements[J]. Journal of Rail and Rapid Transit, 2011, 225(1): 1-13. doi: 10.1243/09544097JRRT398
    [9]
    MOLODOVA M, LI Z L, DOLLEVOET R. Axle box acceleration: measurement and simulation for detection of short track defects[J]. Wear, 2011, 271(1/2): 349-356.
    [10]
    DING Jian-ming, LIN Jian-hui, WANG Guang-ming, et al. Time-frequency analysis of wheel-rail shock in the presence of wheel flat[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1(6): 457-466. doi: 10.1016/S2095-7564(15)30296-8
    [11]
    TSAI H C, WANG C Y, HUANG N E, et al. Railway track inspection based on the vibration response to a scheduled train and the Hilbert-Huang transform[J]. Journal of Rail and Rapid Transit, 2014, DOI: 10.1177/0954409714527930.
    [12]
    HU Ai-jun, TANG Gui-ji, AN Lian-suo. De-noising technique for vibration signals of rotating machinery based on mathematical morphology filter[J]. Chinese Journal of Mechanical Engineering, 2006, 42(4): 127-130. (in Chinese) doi: 10.3321/j.issn:0577-6686.2006.04.023
    [13]
    SERRA J, VINCENT L. An overview of morphological filtering[J]. Circuits, Systems and Signal Processing, 1992, 11(1): 47-108. doi: 10.1007/BF01189221
    [14]
    ZHANG Li-jun, XU Jin-wu, YANG Jian-hong, et al. Adaptive multiscale morphology analysis and its application in fault diagnosis of bearings[J]. Journal of University of Science and Technology Beijing, 2008, 30(4): 441-445. doi: 10.3321/j.issn:1001-053X.2008.04.022
    [15]
    LI Bing, ZHANG Pei-lin, WANG Zheng-jun, et al. Gear fault detection using multi-scale morphological filters[J]. Measurement, 2011, 44(10): 2078-2089. doi: 10.1016/j.measurement.2011.08.010
    [16]
    ZHANG Li-jun, XU Jin-wu, YANG Jian-hong, et al. Multiscale morphology analysis and its application to fault diagnosis[J]. Mechanical Systems and Signal Processing, 2008, 22(3): 597-610. doi: 10.1016/j.ymssp.2007.09.010
    [17]
    SAMANTA B, AL-BALUSHI K R, AL-ARAIMI S A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection[J]. Engineering Applications of Artificial Intelligence, 2003, 16(7/8): 657-665.
    [18]
    LI Zhong-ji, WEI Lai, DAI Huan-yun, et al. Identification method of wheel flat based on Hilbert-Huang transform[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 33-41. (in Chinese) doi: 10.3969/j.issn.1671-1637.2012.04.005
    [19]
    MAZILU T. A dynamic model for the impact between the wheel flat and rail[J]. UPB Scientific Bulletin Series D: Mechanical Engineering, 2007, 69(2): 45-58.
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (893) PDF downloads(734) Cited by()
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return