Variable-Bandwidth Self-Convergent Variational Mode Decomposition and its Application to Fault Diagnosis of Rolling Bearing
Yong Lv, Zhaolun Li, Rui Yuan, Qixiang Zhang, Hongan Wu
Abstract
Variational mode decomposition (VMD) gained popularity due to its excellent performance in rolling bearing fault diagnosis. To obtain accurate diagnosis results depend on proper parameter selection, an improved VMD is proposed to achieve adaptive optimal parameter selection. This algorithm is based on a variable bandwidth control parameter strategy and a center frequency adaptive convergence strategy. First, a variable-bandwidth strategy is constructed according to the frequency distribution difference of each component. Next, the convergence property of the signal is analyzed by a self-convergent strategy based on the variable bandwidth control parameters. Then, the optimal initial center frequencies are discriminated to generate the optimal parameters. Finally, the optimal parameters for the improved VMD are used to obtain the decomposed modes. The validity of the proposed method is demonstrated by one simulation research and two application case analyses of faulty bearings. The performance comparisons indicate that the proposed method provides more accurate, robust, and efficient results.