A bearing fault feature extraction method based on optimized singular spectrum decomposition and linear predictor

Due to the actual bearing working condition is changeable and complex, periodic impulse characteristics of bearing vibration data are easy to be contaminated by background noise and interference harmonics, which indicates that it is difficult to extract directly the fault feature information from the raw bearing vibration data. To address this issue, this paper proposes a bearing fault feature extraction method based on optimized singular spectrum decomposition (OSSD) and linear predictor (LP). Firstly, an LP is employed to preprocess the original bearing vibration data, which can eliminate effectively the influence of noise interferences in bearing vibration data on fault diagnosis. Secondly, the assisted indicator-based OSSD is presented to decompose the pretreated bearing vibration data into a series of singular spectrum components (SSCs), which can overcome the shortcomings of manual setting of mode number K in the original singular spectrum decomposition (SSD). Finally, the correlation kurtosis (CK) indicator is calculated to evaluate the richness of periodic impulses contained in each mode components, where the SSC component with the largest CK value is selected as the optimal SSC. Meanwhile, envelope demodulation analysis of the optimal SSC is conducted to extract bearing defect frequencies and judge bearing fault categories. The effectiveness of the proposed method is validated by using simulation analysis and two experimental cases. Experimental results show that the proposed method can extract effectively bearing defect frequencies. Moreover, fault feature extraction capability of the proposed method is better than that of the original SSD and four popular methods (i.e. parameter optimized variational mode decomposition, morphological filtering enhanced empirical wavelet transform, spectral kurtosis and singular value decomposition) involved in this paper.