基于DS-VMD及相关峭度的滚动轴承故障诊断

In order to adaptively determine the parameters of variational mode decomposition (VMD) and reduce the dependence on prior knowledge in signal processing, a parameter optimization-based VMD and signal reconstruction methods by correlated kurtosis indicators is proposed to extract the fault characteristics of rolling bearings. Firstly, the DS algorithm is used to optimize the parameter combination of the VMD, after which the vibration signal is decomposed to obtain the intrinsic mode function (IMF). Then the correlation kurtosis of each IMF is calculated and used to reconstruct the vibration signal. Finally, the envelope spectrum analysis of the reconstructed signal is performed to extract bearing fault features. The proposed method is compared with empirical mode decomposition (EMD) and conventional VMD method, and both the simulation signal and vibration signal show that the proposed method can effectively identify the fault characteristic frequency of the rolling bearing. Furthermore, compared with the widely used fast kurtogram method, the proposed method also shows better results.