The Hybrid Method of VMD‐PSR‐SVD and Improved Binary PSO‐KNN for Fault Diagnosis of Bearing

Fault diagnosis of bearing based on variational mode decomposition (VMD)‐phase space reconstruction (PSR)‐singular value decomposition (SVD) and improved binary particle swarm optimization (IBPSO)‐K‐nearest neighbor (KNN) which is abbreviated as VPS‐IBPSOKNN is presented in this study, among which VMD‐PSR‐SVD (VPS) is presented to obtain the features of the bearing vibration signal (BVS), and IBPSO is presented to select the parameter K of KNN. In IBPSO, the calculation of the next position of each particle is improved to fit the evolution of the particles. The traditional KNN with different parameter K and trained by the training samples with the features based on VMD‐SVD (VS‐KNN) can be used to compare with the proposed VPS‐IBPSOKNN method. The experimental result demonstrates that fault diagnosis ability of bearing of VPS‐IBPSOKNN is better than that of VS‐KNN, and it can be concluded that fault diagnosis of bearing based on VPS‐IBPSOKNN is effective.