Adaptive energy-constrained variational mode decomposition based on spectrum segmentation and its application in fault detection of rolling bearing

Variational mode decomposition (VMD), a practical adaptive signal decomposition method, has been widely concerned in the fault detection of rolling bearings. However, the performance of the VMD algorithm is highly dependent on the input parameters: the number of modes, the penalty parameter and even the initial center frequency (ICF). In addition, the decomposition residual is an inevitable outcome of signal decomposition, especially in the presence of background noise. How to reduce the mode information contained in the decomposition residual to guarantee the fully extracted modes is also an important factor to be considered to improve the performance of the algorithm. Accordingly, this paper proposes an adaptive energy-constrained VMD method based on spectrum segmentation for rolling bearing fault detection. The proposed method can not only automatically determine the above three input parameters by using the Fourier spectrum segmentation algorithm and Gini index, but also make the energy of each mode more concentrated, thereby effectively suppressing the spectrum overlap between modes. The numerical simulation and experimental data are used to verify the effectiveness of the proposed method. The comparison with some existing adaptive signal decomposition methods demonstrates the superiority of the proposed method. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

An adaptive energy-constrained VMD method based on spectrum segmentation is proposed for rolling bearing fault detection, which effectively extracts signal features of rolling bearing faults. The method can automatically determine important input parameters and concentrate the energy of each mode.