Research On Empirical Mode Decomposition Theory And Its Application

This paper studied the empirical mode decomposition (EMD) method. After summarizing recent research hotspots and progresses about EMD, the errors of EMD's intrinic mode function(IMF) s' amplitude envelope and instantaneous frequency obtained by Hilbert transform were analyzed. The advanced discrete Teager energy operator (TEO) was put forward to improve the computation precision of the amplitude envelope and instantaneous frequency. The zero-crossing and extremum estimation (ZCEE) algorithm was put forward to compute the amplitude envelope and instantaneous frequency. The discrete time-frequency resolution of EMD's time-frequency spectrum was discussed. With advanced discrete TEO algorithm and ZCEE method being introduced to EMD theory, the time-frequency attribution of the time-frequency amplitude spectrum and marginal spectrum obtained by those three methods were compared.Combined with the self-adapting filtering attribution of EMD, the delayed autocorrelation and the slice of Wigner-Ville distribution (WVD) algorithm, named as the de-noise pre-disposal method, were introduced to deal with the noised signal demodulation problem. The delayed autocorrelation function is the longer time interval part of the unbiased estimation of signal's autocorrelation function which is intercepted by rectangle window. The slice of WVD function is the slice at the zero frequency of WVD. Then the result was filtered adaptively by EMD method and the intrinsic mode functions (IMFs) were obtained. The demodulation result was thus obtained by applying Hilbert transform to IMFs. It is shown that the proposed method is more effective to de-noise and to make the modulation information more clear than direct modulation or delayed autocorrelation demodulation.Demodulated resonance method is an important method to identify rolling bearing fault but It must use a band-pass filter, of which the center frequency is one component's intrinsic frequency, to filtrate original signal. Sometimes the frequency components of the added signals near the modulated sideband are not filtrated. Thus there will be wrong diagnosis or unanalysable frequency components on the demodulation spectrum. Therefore the demodulated resonance method based on EMD was proposed. After the vibration signal of rolling bearing was filtered by band-pass filter, the EMD method was introduced to filter the signal self-adaptively and to extract the modulated information. At last, the fault frequency was extracted by Hilbert transform. Simulation and experiment results show that, compared with the method of band-pass filter, this method can make fault frequency more clear and avoid fault diagnoses.An approach based on EMD and principle component analysis (PCA) was proposed to deal with the rotation machine blind sources separation (BSS) problem in the case of nonlinear,non-stationary, noisy source mixing and the number of observed mixtures being less than that of contributing sources. EMD method extracted all oscillatory modes embedded in the observed signals. PCA aggregated similar modes into unifying components. The method was applied to analyze simulation signals and two-way rolling bearing acceleration vibration signals. Result shows that this method can efficiently identify rotation machine's fault characteristic frequencies and the application scope of this method is larger than that of independent component analysis (ICA) method.