Research on fault diagnosis of rolling bearing based on the MCKD-SSD-TEO with optimal parameters

To address challenges in fault diagnosis of rolling bearing caused by great noise contamination and difficult extraction of fault character frequency, a fault diagnosis method of rolling bearing based on the maximum correlation kurtosis deconvolution (MCKD), singular spectral decomposition (SSD) and teager energy operator (TEO) with optimal parameters was proposed in this study. First of all, denoising was performed as a preprocessing to the original vibration signals which were collected by using the MCKD with optimal parameters to highlight the impact component. Next, SSD was performed to the preprocessed signals and the optimal components were selected according to variance contribution. Finally, the energy spectra of optimal components were calculated and characteristic frequency was extracted to realize fault diagnosis of bearing. Through simulation and experimental analysis, the proposed method was proved feasible. It was further compared with empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD), which proved superiority and validity of the proposed method.