文献类型：期刊文献

英文题名：Loading-Based Principal Component Selection for PCA Integrated with Support Vector Data Description

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

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

年份：2015

卷号：54

期号：5

起止页码：1615

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20150800547941);WOS:【SCI-EXPANDED(收录号:WOS:000349580300025)】；

基金：The authors gratefully acknowledge the support of the following foundations: the 973 project of China (2013CB733605), the National Natural Science Foundation of China (21176073), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Data description - Numerical methods - Vectors

摘要：Given that numerous variables exist in industrial processes, it is difficult to make out what real relationships are among the variables. In the principal component analysis (PCA) approach, the loading matrix can reveal inner relations between variables and components, and different components contain different information about a certain variable. Therefore, this study proposes a novel method that respectively selects principal components (PCs) for each variable according to the loadings. The PCs containing more information about a certain variable are selected to construct the subspace for the corresponding variable, and then support vector machine data description (SVDD) technique is adopted to examine the variations in all subspaces. Additionally, a corresponding contribution plot is developed to identify the root cause. Finally, two case studies, a numerical example and the Tennessee Eastman (TE) system, demonstrate the effectiveness of the proposed method, with other PCA-based methods listed for comparison.