The necessity of a non-linear & non-stationary data processing in engineering and the new Hilbert Huang Transform (HHT)

Data analysis is indispensable for engineering, for data is the only link between the theory and the reality. Traditional data analysis methods such as Fourier analysis are all based on linear and stationary assumptions i.e. the signal to be processed must be linear and temporally stationary; otherwise, the resulting Fourier spectrum will make little physical sense. Practical signals such as speech, machine vibrations, biomedical measurement and communications most likely to be both nonlinear and non-stationary. Hence new methods are needed to analyze the data from nonlinear and non-stationary process. Hilbert-Huang Transform(HHT) is a new data processing technology developed by NASA Goddard Space Flight Center. The HHT is derived from the principles of empirical mode decomposition (EMD) and the Hilbert Transform. This paper presents the suitability of using HHT for nonlinear and non-stationary data analysis. The efficiency of the new method is tested on a set of vibration data collected from Westland helicopter gearbox, a non-linear and non-stationary system. Both simulation and the experimental results indicate that this new method HHT is more suitable for non-linear and non-stationary process.

[1]  Fulei Chu,et al.  VIBRATION SIGNAL ANALYSIS AND FEATURE EXTRACTION BASED ON REASSIGNED WAVELET SCALOGRAM , 2002 .

[2]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Minfen Shen,et al.  A method for estimating the instantaneous frequency of non-stationary heart sound signals , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[5]  Gary G. Yen,et al.  Wavelet packet feature extraction for vibration monitoring , 2000, IEEE Trans. Ind. Electron..

[6]  ' FrancisH.Y.Chan,et al.  A METHOD FOR ESTIMATING THE INSTANTANEOUS FREQUENCY OF NON-STATIONARY HEART SOUND SIGNALS , 2004 .

[7]  P. Tse,et al.  An improved Hilbert–Huang transform and its application in vibration signal analysis , 2005 .

[8]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

[9]  Yuping Zhang,et al.  Hilbert-Huang Transform and Marginal Spectrum for Detection of Bearing Localized Defects , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[10]  Peter Willett,et al.  Signal processing and fault detection with application to CH-46 helicopter data , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).