Polynomial fitting detrended autocorrelation kurtosis of 1.5D spectrum and its application in bearing fault feature extraction

Kurtogram is a typical fault diagnosis algorithm, and its derivative methods are widely used in bearing fault signal processing. The key to such methods is to select the indexes that are specifically sensitive to periodic fault characteristics. The traditional time domain kurtosis index is too sensitive to non-periodic transient impulses, and the frequency domain index is too sensitive to harmonic components, which seriously affects the selection of resonance frequency band and the extraction of fault features. In view of this, a polynomial detrended autocorrelation kurtosis of 1.5D spectrum is proposed in this paper. Firstly, the original signal is decomposed by wavelet packet decomposition, and the 1.5D spectrum of each subband signal is calculated. Then, the 1.5D spectral autocorrelation coefficient of each subband is calculated, and the trend term is removed from the specific interval of autocorrelation coefficient by polynomial fitting. Finally, the kurtosis of the autocorrelation coefficient after removing the trend term is calculated to select the optimal resonance frequency band for analysis, so as to extract the fault feature information. The experimental data show that this method is sensitive to the fault characteristic frequency and frequency doubling with coupling relationship, and can suppress non-periodic transient impulse and harmonic interference.