Research on Early Fault Diagnosis of Rolling Bearing Based on VMD

Abstract

Rolling bearings are important parts of rotating machinery. When the early failure occurs, it is difficult to effectively extract the weak fault features. Aiming at this problem, an early fault diagnosis method of Parameter-optimized Variational Mode Decomposition (VMD) of is proposed. First, the instantaneous frequency mean Judged method is used to determine the value of modal number K, and then the fault diagnosis signal is processed by VMD method. By selecting the components of the intrinsic modal function obtained by decomposing the fault signal of bearing that an envelope analysis is performed on the sensitive components to determine the fault type and severity of the bearing. Finally, compared with the results obtained by the empirical modal decomposition algorithm, it is proved that VMD can successfully extract the early fault features of rolling bearing and realize the diagnosis of early fault of bearing.