title: Plant-wide process monitoring based on mutual information-multiblock principal component analysis.
abstract: Multiblock principal component analysis (MBPCA) methods are gaining increasing attentions in monitoring plant-wide processes. Generally, MBPCA assumes that some process knowledge is incorporated for block division; however, process knowledge is not always available. A new totally data-driven MBPCA method, which employs mutual information (MI) to divide the blocks automatically, has been proposed. By constructing sub-blocks using MI, the division not only considers linear correlations between variables, but also takes into account non-linear relations thereby involving more statistical information. The PCA models in sub-blocks reflect more local behaviors of process, and the results in all blocks are combined together by support vector data description. The proposed method is implemented on a numerical process and the Tennessee Eastman process. Monitoring results demonstrate the feasibility and efficiency.
authors: Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, P.O. Box 293, MeiLong Road No. 130, Shanghai 200237, PR China. Electronic address: jiang_qch@hotmail.com.
authors: Qingchao
authors: Q
authors: Jiang
authors: Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, P.O. Box 293, MeiLong Road No. 130, Shanghai 200237, PR China. Electronic address: xfyan@ecust.edu.cn.
authors: Xuefeng
authors: X
authors: Yan
keywords: Multiblock PCA; Mutual information; Plant-wide process monitoring; Support vector data description