Application of Genetic Algorithm to Optimize Variational Mode Decomposition in Bearing Fault Feature Extraction
In the process of Variational Mode Decomposition(VMD),the number of modal components and the size of penalty parameters depend on prior knowledge,so single or sequential optimization of a single parameter may lead to local optimality.In this paper,taking envelope entropy and envelope kurtosis factor as fitness functions,and using the characteristics of global optimization of genetic algorithm,the number of modal components and the combination of penalty parameters of VMD are optimized.Multiple Intrinsic Mode functions(IMF)can be obtained by decompressing the signal through VMD under the optimal parameter combination.The IMF component with the smallest fitness function is selected as the effective IMF component for envelope demodulation,from which fault characteristic frequencies of bearing signals are extracted.The analysis of various bearing fault signals and the comparison with other methods show that the proposed method can effectively extract bearing fault features,which is helpful to accurately extract bearing fault feature frequencies under weak fault conditions.
万方数据
维普
