WEAK FAULT FEATURE EXTRACTION OF ROLLING BEARING BASED ON PARAMETER OPTIMIZED MOMEDA AND CEEMDAN

Aiming at the problem that the fault feature information of rolling bearing is weak under the strong background noise environment,and the single use of the complete ensemble empirical mode decomposition with adaptive noise（ CEEMDAN）method is not effective in extracting the fault feature,a method based on parameter optimized multi-point optimal minimum entropy deconvolution adjusted（ POMOMEDA） and CEEMDAN was proposed. Since the filter effect of MOMEDA is greatly affected by its parameters — fault period T and filter length L,the variable step size search method was proposed to optimize them. Firstly,the fault period T and filter length L of MOMEDA were selected by using multi-point kurtosis and permutation entropy to realize adaptive MOMEDA noise reduction for the original signal. Then,the CEEMDAN method was used to decompose the de-noised signal,and the signal reconstruction was carried out by selecting the intrinsic mode function（ IMF） containing rich fault information according to the weighted kurtosis（ WK） index. Finally,the reconstructed signal was analyzed by envelope spectrum and the fault feature information was extracted. The analysis results of simulated signals and measured signals show that the proposed method is able to extract the weak fault feature frequency of rolling bearings and has certain reliability.