Fault diagnosis method of rolling bearings based on VMD and MDSVM

Abstract

Rolling bearings are one of the most vulnerable parts in rotating machines. This paper presents a novel approach to identify the rolling bearings fault based on variational mode decomposition (VMD) and Mahalanobis distance support vector machine (MDSVM). In this work, since the original vibration signal contains a lot of noise, we use wavelet threshold method to denoise the original vibration signal. The vibration signals are generally non-linear, to extract feature, VMD has been employed to reconstruct signals. When raw signals are decomposed by VMD, according to the center frequency of each decomposed mode, the number of modes is selected. Then we calculate the sample entropy of the decomposed modal component, which is considered as the feature and input of support vector machine (SVM). The Euclidean distance is usually used in the calculation of the Gaussian kernel function of the SVM, which cannot measure the distance between two samples accurately, so we combine the Mahalanobis distance with SVM, construct a Gaussian function kernel based on Mahalanobis distance, and propose a classifier model based on Mahalanobis distance Gaussian function kernel. The model integrates the parameter solutions of the Mahalanobis distance function and the support vector machine into the same framework, which makes full use of the advantages of both and makes it easier to get the solution of the parameters. Finally, all feature vectors are utilized to train improved SVM, with which the fault modes of rolling bearings are identified. The experimental results show that the proposed method has better diagnosing performance.