An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing

Abstract

• A parametric adaptive VMD method based on Hybrid Grey Wolf Optimizer is proposed. • The improved VMD method can effectively and significantly extract the subtle early fault feature. • SVD is a good tool for extracting the robust features. • DBN has higher correctness and stability for recognizing faults than other traditional classifiers. • This new fault diagnosis model (IVMD-SVD-DBN) has higher recognition accuracy than other combination methods. The selection of penalty parameters along with the number of components in the Variational Modal Decomposition (VMD) determines the decomposition effect to a large degree. In order to achieve the optimal selection of relevant parameters in VMD, an improved parametric adaptive VMD method based on Hybrid Grey Wolf Optimizer (HGWO) is proposed. First of all, taking the minimum local envelope entropy of modal components in the VMD as the optimization goal, HGWO algorithm is used to search for the optimal parameter combination in VMD. Then, the improved VMD is used to decompose the vibration signal to obtain modal components. For improving the stability of fault feature, the initial eigenmatrix composed of the key modal components are decomposed by Singular Value Decomposition (SVD), and the singular value is taken as the final eigenmatrix. Finally, the feature matrix is input to the Deep Belief Network (DBN) for learning and training, so as to realize the early fault diagnosis of rolling bearing. The comparative experimental analysis shows that the improved VMD method after parameter optimization can extract the early failure characteristics of rolling bearing more distinctly, and the fault diagnosis model based on this method has higher accuracy and application value.