Research on the Fault Diagnosis Method of Rolling Bearings Based on GAVOA‐VMD

Aiming at the difficulty of fault feature extraction and low fault accuracy of rolling bearing, a method of optimized variational mode decomposition (VMD) based on the improved African vulture algorithm was proposed to extract bearing fault features efficiently. First, the Gaussian random walk and adaptive parameter optimization algorithm for African vultures (GAVOA) is introduced into the algorithm. Second, GAVOA is used to optimize the penalty factor and modal component of VMD. This algorithm can effectively avoid the local optimal solution and improve the search efficiency. Finally, the feature vector is used as the input of CNN‐BiLSTM to identify the fault types of rolling bearings. The experimental results show that the GAVOA‐VMD‐CNN‐BiLSTM fault diagnosis model proposed in this paper has better performance, and the fault diagnosis accuracy can reach 99.667%.