Feature extraction method of rolling bearing fault based on VMD optimized by enhanced SSA and envelope analysis

Aiming at the problem of rolling bearing feature extraction difficulty, this paper proposes a feature extraction method based on ESSA and envelope analysis. Firstly, to address the problem that SSA algorithm tends to fall into local optimum, ESSA is proposed by introducing improvement strategies. And ESSA is applied to optimize the VMD parameters to obtain the optimal parameter combination [K, α]. According to optimal parameters, signal decomposition is performed. Next, the EE values of each IMF are calculated to choose the best component which contains the most fault information. Finally, envelope analysis is performed on the best component to extract fault features. The experimental result shows that the proposed ESSA algorithm improves the signal decomposition effect, and the feature extraction method proposed in this paper can effectively extract the features in the bearing fault signal.