Early Fault Diagnosis of Rolling Bearings Based on Parametric Optimized VMD-MCKD
Aiming at the problem that early fault feature of rolling bearings is easy to be affected by strong background noise and is difficult to be extracted,an early fault diagnosis method for rolling bearings was proposed.In this method,the Archimedes optimization algorithm (AOA) was employed to optimize the parameters of the variational mode decomposition (VMD) and the maximum correlated kurtosis deconvolution (MCKD).Firstly,the correlated kurtosis under different shifts was compared with existing indicators,and the optimal correlated kurtosis indicator was used as the objective function to op-timize the decomposition layer K and penalty factor in VMD algorithm,and the optimal component was selected based on the results of VMD.Secondly,a weighed envelope spectrum kurtosis was proposed as the objective function to optimize the filter length L and the impulse signal period T in MCKD algorithm,and the impulse components were enhanced based on the MCKD algorithm.Finally,the type of rolling bearing faults was determined by envelope spectrum analysis.Simulation and experimental results show that the proposed method can effectively extract and enhance the fault components,and realize the early fault diagnosis of rolling bearings under strong background noise.
万方数据
维普
