文献类型：期刊文献

英文题名：Multi-subspace factor analysis integrated with support vector data description for multimode process monitoring

作者：Wang, Bei[1,2];Li, Zhichao[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai, Peoples R China;[2]Shanghai Elect Windpower Grp, Shanghai, Peoples R China

年份：2018

卷号：355

期号：15

起止页码：7664

外文期刊名：JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

收录：;EI(收录号：20183505755791);WOS:【SCI-EXPANDED(收录号:WOS:000445189100035)】；

基金：The authors gratefully acknowledge the support of the following foundations: National Natural Science Foundation of China (21878081) and the Fundamental Research Funds from the China Scholarship council.

语种：英文

外文关键词：Chemical industry - Data description - Process monitoring - Numerical methods - Vectors - Chemical analysis - Correlation methods - Factor analysis - Multivariant analysis

摘要：In modern plant-wide systems, chemical industry processes are usually equipped with multiple operating modes to meet the requirements of diversification products. Accurately identifying the running-on mode therefore becomes a focal point. Meanwhile, systems produce numerous process variables, along with complex relationships, which may deteriorate the effectiveness with which statistical processes are monitored. To solve this problem, this study proposes a multimode factor analysis (FA) method that integrates tegrates Pearson's correlation coefficient, joint probability, and support vector data description (SVDD). First, subspaces are generated automatically by using Pearson's coefficients of correlation among variables, instead of based on prior knowledge, which is not always available. Second, the statistical indices are derived by the FA models constructed in each subspace and each mode. Third, the running-on mode is identified according to the joint probabilities among the statistical indices. Finally, SVDD is adopted to provide an intuitive indication for fault detection. The efficiency and availability of the proposed method are demonstrated by three case studies: a numerical simulation, the continuous stirred-tank reactor (CSTR) model, and the Tennessee Eastman (TE) benchmark process. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.