A Rotor Fault Diagnosis Method Depending on Local Mean Decomposition and Singular Value Entropy

Abstract

The accuracy and efficiency of rotor fault diagnosis can be improved by using the local mean decomposition (LMD) in rotor fault features extraction. In this paper, a rotor fault features extraction technique depending on LMD and singular value entropy is proposed. In the first place, the local mean decomposition is implemented to attain multiple amplitude-modulation product (PF) components by decomposing the original vibration signal of the rotor. Then, the PF components are decomposed by singular value decomposition to obtain singular values and the singular value entropy. Finally, input the singular value entropy into the support vector machine (SVM) to identify and classify rotor faults. The results of experiment reveal that this method can extract the characteristics of rotor faults effectively, and identify different rotor typical faults accurately.