文献类型：期刊文献

英文题名：Just-In-Time Reorganized PCA Integrated with SVDD for Chemical Process Monitoring

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：60

期号：3

起止页码：949

外文期刊名：AICHE JOURNAL

收录：;EI(收录号：20140817364210);WOS:【SCI-EXPANDED(收录号:WOS:000331338600011)】；

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. The authors also appreciate the valuable comments and suggestions of the anonymous reviewers.

语种：英文

外文关键词：Process monitoring - Data description - Chemical analysis - Process control

摘要：Although principal component analysis (PCA) is widely used for chemical process monitoring, improvements in the selection of principal components (PCs) are still needed. Given that the determination of complicated and changing fault information is not guaranteed using offline-selected PCs, this study proposes a just-in-time reorganized PCA model that objectively selects the PCs online for process monitoring. The importance of the PCs is evaluated online by kernel density estimation. The PCs indicating more varied information are then selected to reorganize the PCA model. Given that the most useful fault information is concentrated, support vector data description is used to replace traditional statistics, thereby relaxing the Gaussian assumption of the process data. The monitoring performances of the proposed method are evaluated under three cases. Compared with conventional PCA methods, more varied information is captured online, and the monitoring performances are improved. ? 2014 American Institute of Chemical Engineers.