An improved variational mode decomposition method based on spectrum reconstruction and segmentation and its application in rolling bearing fault diagnosis

Abstract

Variational mode decomposition (VMD) is an adaptable signal decomposition approach. VMD has excellent noise abatement performance, which is frequently used to identify rolling bearing failures. The primary problem with VMD is the incapacity to identify the number of modes in an adaptive way, which can seriously affect the decomposition effect. To overcome this problem, an improved VMD method based on spectrum reconstruction and segmentation (SRAS-VMD) is presented in this paper. First, the spectrum is simplified by spectrum reconstruction and its energy spectrum is extracted for spectrum pre-segmentation. Then, the boundary fusion is performed according to Gini index of the squared envelope. The initial center frequency and the number of modes are identified from the fusion results. Finally, the optimal mode is selected using the periodic modulation intensity. The envelope spectrum of the optimal mode is observed, and fault diagnosis is performed. Simulation analyses and experimental signals of bearings studies demonstrate the feasibility of this scheme in diagnosing both single point and composite fault of bearings. The advantageousness of this method is shown by contrast with currently available adaptive signal decomposition techniques.