[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
|