A soft sensor method based on Integrated PCA

Abstract

Feature extraction methods such as Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA), are often used for soft sensor modeling in industrial process with high dimensional data. A kind of soft sensor method based on Integrated Principal Component Analysis (Integrated PCA) is proposed for some weakness of KPCA and that of PCA. This approach combines nonlinear information extracted by KPCA with linear information extracted by PCA and it can not only reduce the dimensionality of input data, but also make full use of linear and nonlinear information. Partial Least Squares (PLS) is used to obtain the final soft sensor model and Particle Swarm Optimization (PSO) is applied to get the optimal parameters of Integrated PCA and those of KPCA. Finally, the proposed method is applied to build soft sensor models of diesel oil boiling point and other industrial objects and is proved to have better ability of generalization by being compared with other feature extraction methods.