Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network

In order to combat the difficulty of fault feature extraction and fault recognition in the field of bearing fault diagnosis, a bearing fault diagnosis method based on improved variational mode decomposition (VMD) and parallel hybrid neural network is proposed, which combines reweighted kurtosis (RK) with variable mode decomposition (VMD) and uses reweighted kurtosis as the evaluation index to select the decomposition times of variational mode decomposition, while removing part of the interference in the fault signal and retaining its impact characteristics. Afterwards, the processed fault data set is brought into a parallel hybrid neural network model with a global average pooling layer (GAP) for feature extraction, feature fusion, and fault classification. The parallel hybrid neural network model can extract fault signal features more comprehensively and improve the accuracy of fault diagnosis, while the global average pooling layer can speed up the training and testing. Experiments on the Xian Jiao tong University (XJTU) and Case Western Reserve University (CWRU) bearing public data sets show that the diagnosis accuracy reaches 99.72% and 99.73%, respectively, indicating that the method has good fault diagnosis accuracy and better diagnosis performance compared with other models.