Variable Sub-Region Canonical Variate Analysis for Dynamic Process Monitoring

Abstract

When all process variables are used to establish one data-based model, some variables have little contributions to the model. When these variables are grouped into a small local variable set to develop a data-based model, their contributions will become large. It is called local variable characteristic. Standard canonical variate analysis (CVA) based process monitoring method doesn’t consider local process variable characteristic. To solve this problem, a variable sub-region based canonical variate analysis (V-CVA) is proposed for dynamic process monitoring by enhancing the information of local variables. The proposed method combines variable sub-region division and Bayesian fusion. First, standard CVA is performed by using all process variables. Then, K-means clustering is adopted to divide process variables into sub-regions based on the mutual information of process variables and canonical variables. For each variable sub-region, CVA is conducted to compute local process monitoring statistics. Finally, local statistics are fused to build ensemble statistics based on the idea of Bayesian inference. Compared with the standard CVA method, the proposed V-CVA method can emphasize the information of local variables, and has better monitoring performance in the canonical variable feature subspace and the prediction residual subspace. Two examples demonstrate the effectiveness of the proposed method.