文献类型：会议论文

英文题名：Plant-wide process monitoring based on mutual information-multiblock principal component analysis

作者：Jiang, Qingchao[1]; Yan, Xuefeng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, MeiLong Road No. 130, Shanghai, 200237, China

语种：英文

外文关键词：Numerical methods - Data description - Process control - Principal component analysis

摘要：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. ? 2014 ISA. Published by Elsevier Ltd. All rights reserved.