Industrial Process Monitoring Based on Parallel Global-Local Preserving Projection with Mutual Information

Abstract

This paper proposes a parallel monitoring method for plant-wide processes by integrating mutual information and Bayesian inference into a global-local preserving projections (GLPP)-based multi-block framework. Unlike traditional multivariate statistic process monitoring (MSPM) methods, the proposed MI-PGLPP method transforms plant-wide monitoring into several sub-block monitoringtasks by fully taking advantage of a parallel distributed framework. First, the original datasets of the process are divided into a group of data blocks by quantifying the mutual information of process variables. The block indexes of new data are generated automatically. Second, each data block is modeled by the GLPP method. The variable information and local structure are well preserved during the whole projection. Third, Bayesian inference is introduced to generate final statistics of the process by the probability framework. To illustrate the algorithm performance, a detailed case study is performed on the Tennessee Eastman process. Compared with the principle component analysis and GLPP-based method, the proposed MI-PGLPP provides higher FDRs and superior performance for plant-wide process monitoring.