Kernel Principal Components Analysis for Early Identification of Gear Tooth crack

In the application of the fault diagnosis, principal components analysis (PCA) is often used to judge the state of a equipment and classify the faults by means of projecting the original data to the principal components space. However, if the data are concentrated in a nonlinear subspace, PCA will fail to work well. Kernel principal components analysis (KPCA) transforms the input data from the original input space into a higher dimensional feature space with the nonlinear mapping, and then uses the nonlinear principal components to classify the state of the equipment. In this paper a case of gear fault diagnosis was studied by KPCA. The feature value was firstly extracted from vibration signals of the gearbox under the condition of continue running, and then KPCA method was used to extract the information of gear crack fault. The result shows that KPCA can be more effective to distinguish the state of the gear and more suitable to diagnose the gear faults in early stage