Improved EEMD and overlapping group sparse second-order total variation

Abstract

Strong background noise increases the difficulty in extracting the early fault features of rolling bearing and leads to the signal waveform distortion problem of the total variation denoising method (TVD). Therefore, this paper presents an ensemble analysis method of fault features that combines improved ensemble empirical mode decomposition (MEEMD) with overlapping group sparse second-order total variation (OGSSTV). Based on typical vibration signals with background noise, the effects of mode mixing, reconstruction error, and noise reduction on MEEMD and OGSSTV methods were analyzed and the suitable parameters for fault feature extraction of vibration signals were determined. On this basis, the proposed method was used to extract motor bearing fault features. Simulation results and experimental data showed that the proposed method could suppress mode mixing, reduce the reconstruction error, and solve the waveform distortion problem caused by TVD in the process of signal noise reduction.