An optimal variational mode decomposition for rolling bearing fault feature extraction

Rolling bearings usually work in tough conditions, which makes the collected vibration signals complex and the fault features weak. Hence, fault feature extraction methods for rolling bearings have become a research focus. In this paper, a new method termed optimal variational mode decomposition (VMD) is proposed to extract rolling bearing fault features. Firstly, since envelope entropy is very sensitive to fault signal features, envelope entropy is used as a fitness function, which is an objective function for the whale optimization algorithm (WOA). Secondly, the WOA has numerous merits, such as simple operation, fewer adjustment parameters and a strong ability for jumping out of the local optimum, and it is applied to the optimization of VMD. Finally, intrinsic mode function components are processed through a Teager energy operator. The proposed method is employed to analyze the experimental signal collected from rolling bearings. The comparison results show that the proposed method is more effective and demonstrates superiority over empirical mode decomposition, local mean decomposition and wavelet packet decomposition.