Fault diagnosis of rolling bearing under strong background noise based on SSA-VMD-MCKD

Here, to effectively extract weak fault features of rolling bearing and accurately diagnose faults under strong background noise, a rolling bearing fault diagnosis method combining singular spectral analysis (SSA), variational mode decomposition (VMD) and maximum correlated kurtosis deconvolution (MCKD) was proposed. Firstly, SSA algorithm was used to decompose fault signal, and decomposed signals were screened and reconstructed according to the time domain cross-correlation criterion. Secondly, the whale optimization algorithm (WOA) was used to optimize parameters alpha, K of VMD and L and M of MCKD, respectively. The VMD with optimized parameters was used to decompose the reconstructed signal, and fault feature signals were extracted from intrinsic mode functions(IMFs) obtained with decomposition according to the kurtosis index. Thirdly, the MCKD with optimized parameters was used to enhance fault characteristics. Finally, fault diagnosis was performed using spectrum envelope. Simulation and tests showed that the proposed method can effectively extract and diagnose bearing faults under strong noise interference. © 2023 Chinese Vibration Engineering Society. All rights reserved.