Research on Fault Diagnosis Method of Rolling Bearing Based on Variational Mode Decomposition and Gath-Geva Clustering

Abstract

For the hassle that the fault of the rolling bearing is challenging to identify, a method for identifying the damage degree of rolling bearing based on the combination of Variational Mode Decomposition (VMD) and Gath-Geva (GG) fuzzy clustering is proposed. The method decomposes the known rolling bearing fault signal by VMD, determines the number of decomposed modes based on the magnitude of the component frequency center, and forms the obtained eigenmode components into an initial eigenmatrix for singular value decomposition. The three largest singular values are selected as the input of the GG clustering algorithm, and the membership matrix and cluster center of the known fault signals are obtained. The damage degree of the rolling bearing is identified by the Hamming closeness between the initial membership matrix of the signal to be measured and the cluster centers with clear fault signal. Rolling bearings vibration data verify the effectiveness of the method. The experimental results show that the method in this paper can accurately extract the fault information contained in the vibration signal, realize the correct fault type identification, and provide a reliable basis for the rolling bearing fault diagnosis method.