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高速列车齿轮箱振动特性分析与故障识别方法

万国强 林建辉 易彩

万国强, 林建辉, 易彩. 高速列车齿轮箱振动特性分析与故障识别方法[J]. 机械科学与技术, 2018, 37(1): 115-119. doi: 10.13433/j.cnki.1003-8728.2018.0117
引用本文: 万国强, 林建辉, 易彩. 高速列车齿轮箱振动特性分析与故障识别方法[J]. 机械科学与技术, 2018, 37(1): 115-119. doi: 10.13433/j.cnki.1003-8728.2018.0117
Wan Guoqiang, Lin Jianhui, Yi Cai. Vibration Characteristic Analysis and Fault Diagnosis of High Speed Train Gear-box[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(1): 115-119. doi: 10.13433/j.cnki.1003-8728.2018.0117
Citation: Wan Guoqiang, Lin Jianhui, Yi Cai. Vibration Characteristic Analysis and Fault Diagnosis of High Speed Train Gear-box[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(1): 115-119. doi: 10.13433/j.cnki.1003-8728.2018.0117

高速列车齿轮箱振动特性分析与故障识别方法

doi: 10.13433/j.cnki.1003-8728.2018.0117
基金项目: 

四川省应用基础计划重点项目(2016JY0047)资助

详细信息
    作者简介:

    万国强(1973-),高级工程师,研究方向为动车组技术开发及检修运用技术,wanguoqiang@cqsf.com

Vibration Characteristic Analysis and Fault Diagnosis of High Speed Train Gear-box

  • 摘要: 基于EEMD和HT方法的先进性,将其应用于高速列车齿轮箱振动特性分析与故障识别。采用EEMD和HT方法分析了高速列车齿轮箱的振动特性,包括时域和频域特性。通过与连续小波方法的比较,探讨了EEMD和HT方法在高速列车齿轮箱故障识别与诊断中的应用优势。研究工作中得出的结论:1) EEMD和HT方法能较好地识别高速动车组齿轮的故障特征,比常用的连续小波变换具有良好的应用性能。2)有缺陷齿轮箱的振动幅度明显大于普通齿轮箱,其振动特性发生了显著变化。
  • [1] 张卫华.高速列车耦合大系统动力学理论与实践[M].北京:科学出版社,2013 Zhang W H. Dynamics of coupled systems in high-speed trains:theory and practice[M]. Beijing:Science Press, 2013(in Chinese)
    [2] Evans J, Berg M. Challenges in simulation of rail vehicle dynamics[J]. Vehicle System Dynamics, 2009,47(8):1023-1048
    [3] 陈哲明,曾京.牵引电机转子振动对高速列车动力学性能的影响[J].工程力学,2011,28(1):238-244 Chen Z M, Zeng J. Effect of rotor vibration of traction motor on dynamic behavior of high speed train[J]. Engineering Mechanics, 2001,28(1):238-244(in Chinese)
    [4] McFadden P D. Detection of gear faults by decomposition of matched differences of vibration signals[J]. Mechanical Systems and Signal Processing, 2000,14(5):805-817
    [5] Rai V K, Mohanty A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing, 2007,21(6):2607-2615
    [6] Yu D J, Yang Y, Chen J S. Application of time-frequency entropy method based on Hilbert-Huang transform to gear fault diagnosis[J]. Measurement, 2007,40(9-10):823-830
    [7] Yu D J, Cheng J S, Yang Y. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing, 2005,19(2):259-270
    [8] Peng Z K, Chu F L. Application of the wavelet transform in machine condition monitoring and fault diagnostics:a review with bibliography[J]. Mechanical Systems and Signal Processing, 2004,18(2):199-221
    [9] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A, 1998,454(1971):903-995
    [10] Liu X F, Bo L, Luo H L. Bearing faults diagnostics based on hybrid LS-SVM and EMD method[J]. Measurement, 2015,59:145-166
    [11] Ali J B, Fnaiech N, Saidi L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics, 2015,89:16-27
    [12] Dybała J, Zimroz R. Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal[J]. Applied Acoustics, 2014,77:195-203
    [13] Wang H C, Chen J, Dong G M. Feature extraction of rolling bearing's early weak fault based on EEMD and tunable Q-factor wavelet transform[J]. Mechanical Systems and Signal Processing, 2014,48(1-2):103-119
    [14] Chacon J L F, Kappatos V, Balachandran W, et al. A novel approach for incipient defect detection in rolling bearings using acoustic emission technique[J]. Applied Acoustics, 2015,89:88-100
    [15] Veltcheva A D, Soares C G. Identification of the components of wave spectra by the Hilbert Huang transform method[J]. Applied Ocean Research, 2004,26(1-2):1-12
    [16] Hwang P A, Huang N E, Wang D W. A note on analyzing nonlinear and nonstationary ocean wave data[J]. Applied Ocean Research, 2003,25(4):187-193
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出版历程
  • 收稿日期:  2016-08-19
  • 刊出日期:  2018-01-15

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