Dynamic nonlinear process monitoring based on dynamic correlation variable selection and kernel principal component regression

In actual industrial processes, data are usually time series. Each process variable may have strong autocorrelation and cross-correlation with other variables with different delays. In addition, there are usually complex nonlinearities among variables. To further improve the monitoring performance for dynamic nonlinear processes, establishing a nonlinear filtering model for each variable is necessary. Therefore, a novel dynamic nonlinear process monitoring method based on dynamic nonlinear feature selection and kernel principal component regression (KPCR) is proposed in this study. First, dynamic nonlinear related variables are selected for each variable through mutual information by considering variables with different time delays. Second, process variables are divided into response and independent variable sets. Third, corresponding KPCR models are established to describe the dynamic relationships with the selected dynamic related variables as input variables and with response variables as output variables. To monitor the dynamic processes, kernel principal component analysis model is constructed on the basis of the residuals, where the residuals are obtained by comparing measured values of instruments with the predicted values of KPCR models. A support vector data description model is established to monitor the independent variables. Three cases are used to verify the performance of the novel approach. Results show that the proposed method is superior to and more effective than other advanced dynamic process monitoring methods. © 2022 The Franklin Institute