The research of pattern recognition of gear pump based on EMD and KPCA-SVM

Focusing on feature extraction of non-stationary vibration signals in condition monitoring and fault diagnosis of gear pump, the fault diagnosis approach based on empirical mode decomposition method and kernel principal component analysis (KPCA) and support vector machines (SVM) is proposed. The improved empirical mode decomposition (EMD) method is used to decompose the mechanical equipment output signal into a number of intrinsic mode function (IMF) components and a residue component, and calculated ten dimensionless parameters of each IMF and residue component, then extract result from the original parameters by using KPCA, at last the kernel principal component is classified by inputting the new feature vector to SVM for training and recognizing. The simulation and experiment results show that the advanced method is effective in restraining end effect, and the analysis result of gear pump vibration signals in different conditions validate the method is effective.