An improved simplex-based adaptive evolutionary digital filter and its application for fault detection of rolling element bearings

The de-noising performance and convergence behavior of the adaptive evolutionary digital filter (EDF) are restricted by the factors of constant evolutionary coefficients and taking the reciprocal of average energy of residual signal as the fitness function. In this paper, an improved adaptive evolutionary digital filter based on the simplex method (EDF-SM) is proposed to overcome the shortcomings of the original EDF. A new evolutionary rule was constructed by introducing the simplex-based mutating method and by then combining this with the original cloning and mating methods. The reciprocal of sample entropy was taken as the fitness function and variable evolutionary coefficients were employed. Numerical examples show that the proposed EDF-SM exhibits a higher convergence rate and a better de-noising behavior than the other EDFs. The effectiveness of the proposed method in discovering fault characteristics and detecting faults of rolling element bearings is supported using an experimental test.