A novel condenser fault diagnosis method based on KPCA and multiclass SVMs

A novel fault diagnosis method of condenser based on kernel principle component analysis (KPCA) and multi-class support vector machines (MSVMs) is proposed in this paper. KPCA is applied to MSVMs for feature extraction. It firstly maps data from the original input space into high dimensional feature space via nonlinear kernel function and then extract optimal feature vector as the inputs of MSVMs to solve condenser fault classification problems. A global optimizer, particle swarm optimizer (PSO), is employed to optimize the parameters of MSVMs to improve fault classification accuracy. The experimental results show that the proposed approach can effectively capture the nonlinear relationship among variables and improve the accuracy of fault diagnosis.