A VME method based on the convergent tendency of VMD and its application in multi-fault diagnosis of rolling bearings

Variational mode extraction (VME) is a novel fault diagnosis technology, which can effectively extract a narrow-band mode from the multi-component signal. However, its performance is seriously affected by two initial parameters: the initial guess of center frequency ωd and the balance factor α. To determine these two parameters adaptively, a VME method based on the convergence property of variational mode decomposition (VMD) is proposed for the multi-fault diagnosis of rolling bearings. Firstly, we study the influence of the balance factor on the convergence characteristic of VMD, the VMD convergence tendency chart is established. Secondly, the initial center frequencies of all latent modes in the faulty signal are adaptively determined by the VMD convergence tendency chart; taking kurtosis as the evaluation index, the corresponding balance factor is optimized separately. Finally, VME is used to extract all modes one by one. The experimental analog signal and engineering application signal are analyzed by this new method and compared with ensemble empirical mode decomposition (EEMD), fast kurtogram, and single-mode VMD. The results display that the new method can accurately extract multiple fault features from the bearing compound fault signal, and the fault feature extraction accuracy is better than the above three classical signal processing methods.