Processing Method for  Hilbert-Huang Transform End Effects Self-Adaptive Endpoint-Phase Sinusoidal Extension

Abstract

Abstract: For the Hilbert-Huang Transform(HHT) endpoint effect problem, a self-adaptive method of endpoint-phase sinusoidal extension was presented. This method adaptively adds sinusoidal extension function of phase, amplitude and frequency to improve decomposition precision according to the data trend near the end. Then the object function to represent empirical mode decomposition(EMD) performance was introduced to satisfy the pertinence precision between intrinsic mode function(IMF) and original signal and low iterations of EMD. The boundary extension parameter decomposition is an adaptive process with better practicability. The simulation and example of hydraulic system show that this approach can not only solve HHT end effect, but also improve EMD decomposition precision with less filtration and reduce Hilbert spectrum end effect. Finally, it can extract the fault characteristics of gear pump vibration signal and get a good application effect.

Key words: empirical mode decomposition (EMD), end effects, endpoint extension, fault diagnosis

CLC Number:

TP 274
TH 17

LI Fang-Xi, CHEN Gui-Ming, LIU Xi-Liang, ZHANG Qian, LI Sheng-Chao. Processing Method for Hilbert-Huang Transform End Effects Self-Adaptive Endpoint-Phase Sinusoidal Extension[J]. Journal of Shanghai Jiaotong University, 2013, 47(04): 594-601.

References

［1］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］. Procedures of the Royal Society of London, Series A, 1998,454:903995.
［2］Guo D， Peng Z P. Vibration analysis of a cracked rotor using HilbertHuang transform［J］. Mechanical Systems and Signal Processing, 2007, 21(8):30303041.
［3］Li X L, Bassiunya A M. Flute breakage detection during end milling using HilbertHuang transform and smoothed nonlinear energy operator［J］. International Journal of Machine Tools and Manufacture, 2007, 47(6): 10111020.
［4］Feldman M. Considering high harmonics for identification of nonlinear systems by Hilbert transform［J］. Mechanical Systems and Signal Processing, 2007, 21(2): 943958.
［5］Guo K. Application of EMD method to friction signal processing［J］. Mechanical Systems and Signal Processing, 2008, 22(1): 248259.
［6］Deng Y J , Wang W, Qian C C. Boundaryprocessing technique in EMD method and Hilbert transform［J］. Chinese Science Bulletin, 2001, 46 (11) : 954961.
［7］Huang D J , Zhao J P, Su J L. Practical implementation of HilbertHuang transform algorithm［J］. Acta Oceanologica Sinica, 2003, 22 (1): 114.
［8］Zhao J P，Huang D J．Mirror extending and circular spline function for empirical mode decomposition method［J］． Journal of Zhejiang University(SCIENCE)，2001，2：247252．
［9］杨建文, 贾民平. 希尔伯特黄谱的端点效应分析及处理方法研究［J］. 振动工程学报, 2006, 19 (2) : 283288.
YANG Jianwen， JIA Minping. Study on processing method and analysis of end problem of HilbertHuang spectrum［J］. Journal of Vibration Engineering, 2006, 19 (2): 283288.
［10］Cheng J S，Yu D J，Yang Y．Application of support vector regression machines to the recessing of end effects of HilbertHuang transform［J］． Mechanical Systems and Signal Processing， 2007(21)： 11971211．
［11］Qi K Y， He Z J．Cosine windowbased boundary processing method for EMD and its application in rubbing fault diagnosis［J］． Mechanical Systems and Signal Processing， 2007(21)： 27502760．
［12］胡爱军,安连锁, 唐贵基.HilbertHuang变换端点效应处理新方法［J］.机械工程学报,2008,44(4):154158.
HU Aijun, AN Liansuo, TANG Guiji. New process method for end effects of HilbertHuang transform［J］. Chinese Journal of Mechanical Engineering, 2008, 44(4): 154158.

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Processing Method for Hilbert-Huang Transform End Effects Self-Adaptive Endpoint-Phase Sinusoidal Extension

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