文献类型：期刊文献

英文题名：Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference

作者：Jiang, Qingchao[1];Yan, Xuefeng[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2014

卷号：136

起止页码：121

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616526324);WOS:【SCI-EXPANDED(收录号:WOS:000339534500013)】；

基金：The authors gratefully acknowledge the support from the following foundations: 973 Project of China (2013CB733600), National Natural Science Foundation of China (21176073), Program for New Century Excellent Talents in University (NCET-09-0346) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Multi-mode plant-wide process; Multi-block principal component analysis; Joint probability; Bayesian inference; Mutual information

摘要：A multi-mode plant-wide process monitoring scheme that integrates mutual information (MI)-based multi-block principal component analysis (PCA), joint probability, and Bayesian inference is developed. Given that the prior process is not always available, an MI-based block division method is proposed to divide blocks automatically by considering both cross-relations and high-order statistical information among variables. The PCA monitoring model is established in each sub-block and each mode, and a joint probability based on T-2 statistics is defined to identify running-on mode. Then, the statistics in different sub-blocks are combined by using Bayesian inference to provide an intuitive indication. Finally, an improved contribution plot method is proposed to identify the root cause of faults. The feasibility and efficiency of the proposed method are evaluated by case studies on a numerical process and the Tennessee Eastman benchmark process. Monitoring results and comparisons with conventional PCA methods indicate the superiority of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.