An enhanced minimum entropy deconvolution with adaptive filter parameters for gear fault diagnosis

Abstract

Blind deconvolution is one of the most effective methods in gear fault diagnosis. However, the deficiency of the deconvolution criterion, dependence on prior knowledge, and requirement of appropriate parameters limit the application of the conventional methods to a certain extent. In this paper, an enhanced minimum entropy deconvolution with adaptive filter parameters (EMED-AFP) is proposed. Within EMED-AFP, a nonlinear transformation is developed and incorporated into the iterative solution process of filter coefficients. By incorporating it, fault impulses are enhanced, making the filter estimation more accurate and effective. Moreover, the EMED-AFP is configured with a parameter-adaptive strategy designed to find the optimal filter parameters. In such a context, the method solves the significant issue that conventional methods specify filter parameters empirically. Both simulation and case studies verify the effectiveness of the method for gear fault diagnosis. Meanwhile, compared with some popular methods, EMED-AFP shows superiority in recovering gear fault impulse trains.