A Novel Decentralized Process Monitoring Scheme Using a Modified Multiblock PCA Algorithm

Abstract

Decentralized process monitoring based on purely data-based methods has recently gained considerable attention in multivariate statistical process monitoring circle. Although the process variables can be divided into several blocks automatically according to their statistical preferences, most of the existing multiblock modeling strategies tends to build local monitoring models individually, where the relevance among different blocks is ignored, and this leaves a room for enhancing process monitoring performance. Inspired by the recognition of this lack, a modified multiblock principal component analysis (MBPCA) algorithm is proposed for extracting block scores with respect to both specificity in each block and relevance among different blocks. Based on this sort of modeling strategy, a novel decentralized process monitoring is formulated by incorporating a PCA-based process decomposition strategy for block division, Bayesian inference to achieve decision fusion of fault detection, and reconstruction-based contribution plots for fault diagnosis. The superiority and validity of the proposed method is finally demonstrated through comparison studies on two simulated examples.