Aiming at the challenges of feature extraction from rolling bearing vibration signals and the low accuracy of fault diagnosis,a novel fault diagnosis method based on improved multiscale sample entropy(IMSE)and parameter-optimized variational mode decomposition(VMD)is pro-posed.This method initially employs the IMSE to perform smooth coarse-graining on the original time series,replacing the average value with the maximum value of each sequence to represent the coarse-grained information,thus avoiding the data loss issue inherent in multiscale sample entropy(MSE).By optimizing VMD parameters through a combination of scale spectrum and summation fuzzy entropy,the optimal mode components are obtained,and the reconstruction signal is selected.The IMSE values of the reconstructed signals serve as feature vectors input into a support vector machine for fault diagnosis.Experiment results demonstrate that the proposed method obtains more accurate fault signal features and increases the fault diagnosis accuracy.
fault diagnosis of rolling bearingvariational modal decompositionscale spectrasummed fuzzy entropymultiscale sample entropy
维普
万方数据
