Research on rolling bearing fault diagnosis based onTVFEMD-SVD and random forest algorithm

Abstract

In order to solve the problem that it is difficult to extract the early fault of rolling bearing under the background noise, a rolling bearing fault diagnosis method based on TVFEMD-SVD and random forest algorithm is proposed. In this method, the original vibration signal of the bearing is decomposed into a series of eigenmode components with physical significance by using time-varying filtering empirical mode decomposition. The components with higher fault information are selected according to the kurtosis, and SVD decomposition is carried out. The singular values obtained by decomposition are input into the random forest as eigenvalues for bearing fault diagnosis. Experiments show that this method can effectively diagnose the fault of rolling bearing.