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摘要
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. Note to Practitioners-This paper attempted to tackle an issue derived from data-based decentralized process monitoring of industrial processes. In this paper, we proposed a modified multiblock PCA (MBPCA) algorithm for developing decentralized monitoring models, and integrate them with Bayesian inference to get unique probabilistic monitoring index for fault detection. The presented decentralized monitoring scheme provides a way to model the large-scale process data with the consideration of the specificity in individual block as well as the relevance among different blocks, which is partially ignored by most of the purely data-based decentralized monitoring methods. Furthermore, the comparison studies demonstrated that the proposed method is highly competitive for monitoring large-scale processes.
