Fault Feature Extraction of Rolling Bearing Based on Parameter Adaptive VMD
Aiming at the problem that the fault feature signal extraction of rolling bearing was easily affected by complex working environment and the parameters of variational mode decomposition(VMD)were selected by human experience,a method of fault feature extraction of rolling bearing based on parameter adaptive VMD was proposed.Firstly,the envelope spectral entropy of the intrinsic mode function(IMF)of the original signal after VMD was used as the fitness function,and the cheetah optimizer(CO)algorithm was used to optimize the decomposition order k and penalty factor α adaptively;Secondly,the IMF components were reconstructed based on the kurtosis criterion;Then,Hilbert envelope spectrum analysis was performed on the reconstructed signal to extract fault features,and feasibility was verified through simulation signal and experimental signal.The research results show that this method is more accurate in extracting fault characteristics compared to classical VMD;In terms of parameter optimization,CO algorithm has increased by 65% compared to SSA.The research has certain engineering application value.
rolling bearingfeature extractionVMD(variational mode decomposition)IMF(intrinsic mode function)CO(cheetah optimization)algorithm
万方数据
维普
