Rotor Fault Analysis of Classification Accuracy Optimition Base on Kernel Principal Component Analysis and SVM

Abstract Directing at the problems that it is hard to determine fault type if the vibration of aero-engine's rotor system is over standard during the maintenance testing, a fault diagnosis approach based on kernel principal component analysis (KPCA) feature extraction and multi-class support vector machines (SVM) is proposed. This method first takes the use of nonlinear feature extraction of KPCA to extract the feature of testing cell standard fault samples. By computing the inner product kernel functions of rotor vibration signal's original feature space, the nonlinear map of rotor vibration signal transformed from low dimensional feature space to high dimensional feature space is achieved. The nonlinear principal components of original feature are obtained by performing PCA on the high dimensional feature data. Then, the nonlinear principal components are taken as eigenvectors of multi-class SVM to perform training and test. During the training period, optimize the relatively parameter by adopting cross optimization algorithm to find out the best penalty parameter and kernel function parameter. A high classification accuracy of training set and test set is sustained, overfitting and underfitting are avoided. Experiment results indicated that this method had good performance in distinguishing different fault modes for high-speed rotor, and was suitable for machinery's state recognition.