Rolling Element Bearing Incipient Fault Feature Extraction Based on Optimal Wavelet Scales Cyclic Spectrum

doi:10.3901/JME.2018.17.208

Abstract

Abstract: Rolling Element Bearing fault characteristic information is within the second order cyclic stationary signal. But it is susceptible to noise interference. The method of cyclic periodogram based on the short-time Fourier transform used on the cyclic stationary signal analysis can improve the recognition ability of cyclic failure. But it is not good for weak fault characteristic identification and its results affected by the size of the window function. The method of optimal wavelet scales cyclic spectrum is proposed for detection of rolling element bearing early faults. The continuous wavelet transform is carried on vibration signal processing to obtain the wavelet coefficients firstly. Then, the optimal scale is selected by correlated kurtosis values. And then, the wavelet coefficients in this scale range are analyzed by using cyclic spectra along the time axis. At last, the average value of the cyclic spectra under the optimal scales is calculated for feature extraction. Comparison with the result of cyclic periodogram, it can be concluded that the proposed method has good performance for rolling element incipient fault feature extraction.

Key words: continuous wavelet transform, correlated kurtosis, cyclic periodogram, optimal wavelet scales cyclic spectrum, rolling element bearing

CLC Number:

TH113

YANG Rui, LI Hongkun, HE Changbo, WANG Fengtao. Rolling Element Bearing Incipient Fault Feature Extraction Based on Optimal Wavelet Scales Cyclic Spectrum[J]. Journal of Mechanical Engineering, 2018, 54(17): 208-217.

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