Improved VMD-MCKD Bearing Fault Frequency Detection Algorithm
A bearing fault frequency extraction method is proposed,which combines wavelet packet transform,parame-ter optimized variational mode decomposition(VMD),and maximum related kurtosis deconvolution(MCKD)to address the difficulty in extracting fault frequencies from rolling bearing fault signals under low signal-to-noise ratios.Using the method of wavelet packet transform,the fault signal is decomposed and reconstructed.The proportion of energy in each frequency band signal after reconstruction is calculated.The frequency band signal with the highest energy proportion is selected and optimized using the dung beetle optimizer(DBO)algorithm.The signal is adaptively decomposed and a weighted kurtosis index is constructed to screen the optimal modal components.Then,the optimal modal components are enhanced using the MCKD optimized by DBO.Finally,the enhanced signal is processed using envelope demodulation method and analyzed to diagnose the frequency of bearing faults.The experiment verified that the method proposed in this article achieves fault frequency detection of rolling bearings in strong noise environments.
万方数据
维普
