Rolling bearing signal denoising method based on MCKD-VMD algorithm

Abstract

Abstract In view of the nonlinear and nonstationary characteristics of the weak fault signal of rolling bearings and the characteristics that are easy to be masked by strong background noise, a weak fault diagnosis method of rolling bearings combining Variational Mode Decomposition (VMD) and Maximum Correlated Kurtosis Deconvolution (MCKD) is proposed. In order to realize the adaptive parameter selection of VMD and MCKD, the particle swarm optimization algorithm was used to optimize the parameters in the two algorithms. Firstly, the Particle Swarm Optimization (PSO) was used to optimize the α and K in the VMD algorithm, and then the optimal mode components were selected based on the results of VMD decomposition of weak fault signals. Secondly, the PSO is used to optimize the sum in the MCKD algorithm, and then the fault shock component in the optimal component signal is strengthened based on the MCKD algorithm. Finally, the weak fault characteristics of the bearing were extracted by the envelope spectrum. The experimental results show that this method can adaptively enhance the impact component in the weak fault of the bearing, and effectively extract the weak fault characteristics of the bearing submerged by the strong noise.