Gear fault feature extraction method based on ASMVMD and MOMEDA
Aiming to address the challenge of difficulty in extracting damage features from gear signals due to significant noise interference,a method combining adaptive successive multivariate variational mode decomposition(ASMVMD)and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)was proposed.Firstly,the optimization of SMVMD decomposition parameters was adaptively conducted using the weighted chimpanzee optimization algorithm.The optimal values for the maximum penalty factor α and the maximum decomposition mode number k were determined by using the negative sum of the average crest factor of envelope spectrum(Ec)of each channel component after successive multivariate variational mode decomposition as the fitness function for optimization.Then,the ASMVMD method was used to adaptively decompose the multi-channel fault data of gears.Specific components were then extracted from each channel based on the Ec index,and these components were summed for signal reconstruction.Finally,the signal was reconstructed using MOMEDA deconvolution processing to further enhance the impact characteristics of gear faults,and envelope spectrum analysis was used to deconvolve the signal and extract the characteristic frequency of gear faults.The research results show that through the analysis of simulated signals and experimental signals,it can be concluded that the combined method of ASMVMD-MOMEDA has a significant denoising effect on the signal.This effectively suppresses the impact of irrelevant interference components,allowing the fault frequencies and their first few harmonics to be clearly observed in the envelope spectrum.When comparing with the method that combines multivariate empirical mode decomposition(MEMD)-MOMEDA,the envelope spectrum obtained by the ASMVMD-MOMEDA method is cleaner and its harmonics are more distinct.This further demonstrates that the ASMVMD-MOMEDA method can accurately extract gear fault characteristics.
万方数据
维普
