Mixed kernel principal component weighted regression based on just-in-time learning for soft sensor modeling

Abstract

Soft sensors have been extensively applied for predicting difficult-to-measure quality variables. However, industrial processes are often characterized with the nonlinearity and time variance, which makes it difficult to accurately predict the quality variables. In this paper, a just-in-time learning (JITL) based mixed kernel principal component weight regression (MKPCWR) is proposed for soft sensing of nonlinear and time-variant processes. Firstly, taking advantages of classical principal component analysis and kernel PCA, a mixed kernel principal component analysis (MKPCA) is proposed to extract the nonlinear feature. The raw input space and high-dimensional nonlinear space form a mixed space, from which the MKPCA gets a better nonlinear feature representation for the raw input data. Then, a weighted regression method based on JITL is proposed to deal with the time-variant problem of processes. When building the JITL-based regression model, relevant training samples are assigned correspond weights in the loss function to increase the prediction accuracy of model. Lastly, the effectiveness of the proposed method for soft sensor modeling is demonstrated on two industrial processes. Results show that JITL-based MKPCWR is more effective to solve the nonlinearity and time variance than JITL-based principal component regression and kernel principal component regression.