Fault feature extraction of rolling bearing based on VMD optimized by composite spectral kurtosis
In response to the problem that rolling bearing vibration signal characteristics were difficult to be extracted in the case of strong noise,a method based on composite spectral kurtosis to optimise the variational modal decompositionwas proposed. First,the original fault signal was subjected to variational modaldecomposition,and several intrinsic mode functionswere acquired by optimizing the key parameters of VMD-modal numberand penalty factorrespectively with the principle of the maximum value of composite spectral kurtosis. Then,the kurtosis of each IMFwas calculated,and the component with the maximum kurtosis value was selected as the optimal IMF. Finally,the Hilbert transform was performed on the optimal intrinsic modal function to obtain their envelope spectra,so as to realize the extraction of the fault eigenfrequency. Through the analysis of the public dataset and the relevant data of the homemade test bed,it is shown that the proposed method can effectively extract the fault characteristics of the fault signal under the background of strong noise and realize the discrimination of the fault type.
rolling bearingfeature extractionfault diagnosisvariational mode decompositioncomposite spectral kurtosis
万方数据
维普
