Misalignment Characteristic Analysis Based on Kernel Principal Component Analysis

A new method based kernel principal component analysis (KPCA) is used to extract interesting misalignment features from a dynamical system. In this method, the projections (PCs) of the image of a test point with misalignment onto the nonlinear principal components in normal condition in featured space F are computed to represent the misalignment characteristics. It is shown in this work that the exploitation of the projections combination can improve the detection results. Even the varying trends of misalignment fault could be identified by use of this detection method. The method is illustrated on an experimental example of an auxiliary magnetic bearing rotor system.