Fault Diagnosis for Motor Rotor Based on KPCA-SVM

Aimed at the nonlinear properties of motor rotor vibration signal,a fault diagnosis method based on kernel principal component analysis (KPCA) and support vector machines (SVM) was proposed. Initial feature vectors of motor vibration signal were mapped into higher-dimensional space with kernel function. Then the PCA method was used to analyze the data in the high dimensional space to extract the nonlinear features which is used as training sample of SVM fault classifier. Then the rotor fault is identified using the trained classifier. The classification effect of KPCA-SVM is compared with PCA-SVM and SVM. The result shows that the method based on KPCA-SVM can identify motor rotor fault efficiently and fulfill fault classification accurately.