Bearing failure of reciprocating compressor sub-health recognition based on CAGOA-VMD and GRCMDE

Abstract

The bearing vibration signal of reciprocating compressor has complex, non-stationary, nonlinear, and feature coupling characteristics. A method for sub-health recognition of sliding bearings based on curve adaptive grasshopper optimization algorithm optimize the parameters of variational mode decomposition (CAGOA-VMD) and generalized refine composite multiscale dispersion entropy (GRCMDE) is used. First, the CAGOA was used to search the best influence parameter combination of the VMD algorithm, and determine the bandwidth parameters and the number of decompositions that need to be set by the VMD algorithm, decompose the bearing fault signal to obtain a series of IMF. Then, the kurtosis and correlation coefficient criteria are used to select a group of components that contain the most information, and the fault signal is reconstructed on this component, and then the reconstructed signal is analyzed by GRCMDE to form a fault eigenvector. Finally, KPCA is used for dimensionality reduction to select input features and input into KELM for classification and recognition. The experimental results show that this method can effectively extract the bearing fault features of reciprocating compressors, and the eigenvectors have good separability, and realize the sub-health recognition of bearing fault features of reciprocating compressors.