An improved blind deconvolution method with adaptive filter optimization for gearbox fault diagnosis

Abstract

The traditional deconvolution method can filter out the interference caused by the propagation path and noise from the complex vibration signal to restore the fault excitation characteristics. In order to solve the influence of the initial filter coefficients on the convergence results of the algorithm and improve the accuracy of fault identification, this paper designed filter optimized minimum entropy deconvolution (FOMED) method. Firstly, the method of mode location and initial filter coefficient construction based on spectrum trend is studied to achieve the rough estimation of frequency domain mode. Then, the filter coefficients based on Meyer wavelet are introduced to update the initial filter banks. Simulation results show that the new iterative updating method has better robustness. Finally, through kurtosis of unbiased autocorrelation of square envelope (AC), the fault information in the filtering results is screened and diagnosed. The experimental data verify that the proposed FOMED is suitable for fault detection of planetary gearbox in wind turbine transmission system and rolling bearing in rotating machinery equipment.