Fault diagnosis method for rolling bearing based on VMD and improved SVM optimized by METLBO

Vibration signal processing and classification are critical for bearing fault diagnosis. In this study, a hybrid framework based on multi-envelopment teaching-learning-based optimization (METLBO) was proposed by combining parameters optimized variational mode decomposition (VMD) and improved support vector machines (ISVM). First, the average value of minimum enveloping entropy was considered the objective function of the optimizer, and the optimal parameters of VMD were obtained through METLBO optimization. Next, these optimal parameters were adopted to decompose the fault signal into intrinsic modal functions (IMFs). For ensuring fault feature robustness, the eigenvectors were formed by the energy and envelope entropy of IMFs. Finally, the ISVM model was established for training and testing by adding an input layer to the SVM to perform soft thresholding on input data. METLBO was adopted to determine the optimal soft threshold values of features and hyper-parameters of ISVM. The experimental comparison analysis revealed the effectiveness of the proposed method for bearing fault diagnosis.