Research on rolling bearing fault diagnosis method based on AMVMD and convolutional neural networks

Due to the uncertainty of the actual industrial environment, the effective features of the collected multivariate data are submerged by environmental noise. Due to the limited ability of single signal analysis method to recognize the features of multivariate data. Therefore, this paper proposes a rolling bearing fault diagnosis method based on adaptive multivariate variational mode decomposition (AMVMD) and multi-scale convolutional neural network (Multi-scale CNN). Firstly, based on the multivariate variational modal algorithm, the minimum modal overlapping component (MMOC) index is proposed and used as the objective function to seek the optimal solution of the main parameters of multivariate variational modes, to realize the adaptive decomposition and noise reduction of the original signal. Then, the multi-scale convolutional neural network was used to extract and recognize the denoising feature vectors in a deeper level, and finally the bearing fault diagnosis under complex working conditions is realized. Bearing data from Paderborn University were used to verify the proposed method. The results show that under the same conditions, the fault diagnosis accuracy of AMVMD-MSCNNs can achieve 98.60%, which has certain advantages and practical application significance.