Fault diagnosis of planetary gearbox based on minimum entropy deconvolution and adaptive variational mode decomposition

To solve the problem of low robustness and uncertain decomposition number of variational mode decomposition (VMD) in the fault feature frequency extraction process of planetary gearbox, a gearbox fault diagnosis method based on minimum entropy deconvolution (MED) and adaptive variational mode decomposition (AVMD) was proposed. First, the signal was denoised by the MED to highlight the fault signal characteristics. By using the new definition of the instantaneous frequency and the concept of variation, the series of VMD was adaptively selected. The VMD method was used to decompose the fault signal of the planet gearbox into several intrinsic modal function (IMF) components. According to the analysis of correlation coefficient, the component of IMF with the fault signal was selected to envelope spectrum analysis to extract the fault characteristic frequency. The analysis results of the simulation signal and the experimental signal show that the peak signal-to-noise ratio of the signal is increased by 10% after denoising by using the MED, the error problems of the empirical selection of the number of VMD are solved and the process is self-adaptive. The problem of the low robustness of nonlinear non-stationary signals in the case of the strong noise is solved. The fault characteristic frequency of the wind power gearbox is accurately extracted. © 2020, Editorial Department of Journal of Southeast University. All right reserved.