Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process

Abstract

The nonlinear feature extraction(Kernel Principal Component Analysis(KPCA) and kernel Independent Component Analysis(ICA)) was used to eliminate the uncorrelated component from input data,and reduce dimension in the paper.Kernel principal component analysis adopted a kernel function to map input data into feature space and calculate linear PCA.Kernel independent component analysis extracted independent component by linear ICA transformation in the KPCA whitened space.The extracted features were taken as the input of Least Square Support Vector Machine(LSSVM) classifier.Incorporating nonlinear feature extraction into LSSVM served as a new method for intelligent fault diagnosis.The proposed method was applied to the fault diagnosis of lubricating oil process for a petro-plant.The effectiveness of the proposed method is verified.