Degradation state assessment of rolling bearing based on variational mode decomposition and energy distribution

Degradation state assessment of bearing is an important part of prognostic and health management (PHM) in rotating machinery. Generally, the energy distribution of frequency band is sensitive to degradation state for rolling bearing. Hence, a novel assessment method based on variational mode decomposition (VMD) and energy distribution is proposed in this work. Firstly, the VMD is used to decompose raw vibration signal into several components with different scales and frequency bands. These components is capable of reflecting the local characteristic of vibration signal. Then, the energy distribution of these components is utilized as feature vector. Finally, the different bearing states can be classified by the scatter plots of the first several principal components after principal component analysis (PCA). The analysis of an experimental dataset demonstrates the effectiveness of this methods. The comparative analysis shows the VMD is superior to traditional empirical mode decomposition (EMD) methods.