An optimal filter length selection method for MED based on autocorrelation energy and genetic algorithms

This paper proposed a method for exact selecting the optimal filter length of minimal entropy deconvolution (MED) to solve it recovering a single random pulse when the filter length is not improper. The energy ratio of autocorrelation between the filtered signal and the residual signal is adopted to measure the salience of periodic impulses. Then this index is used as an objective function of genetic algorithms (GA) to form an adaptive optimal selection method of filter length. The proposed method is verified by two different rolling bearing fault experiments. The results show that the proposed method reveals the periodic impulses successfully from the casing signals. Compared with other MED-based methods, the proposed method has better performance in detecting the weak fault signal.