Application of VMD feature fusion in fault diagnosis of rolling bearings

Abstract

In response to the complex nature of bearing faults and the difficulty of a single feature accurately reflecting the overall fault information, this paper proposes a VMD feature fusion method for rolling bearing fault diagnosis. Firstly, use VMD to decompose the bearing vibration signal; Secondly, calculate energy entropy, singular value entropy, permutation entropy, and sample entropy to form a fusion feature vector; Finally, the least squares support vector machine (LS-SVM) is used as a classifier to identify bearing fault types. Through experiments, this method can effectively achieve bearing fault diagnosis.