期刊
JOURNAL OF PROCESS CONTROL
卷 39, 期 -, 页码 88-99出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2016.01.001
关键词
Independent variables; Related variables; Process monitoring; Kernel principal component analysis; Support vector data description
资金
- 973 Project of China [2013CB733600]
- National Natural Science Foundation of China [21176073]
- Program for New Century Excellent Talents in University [NCET-09-0346]
- Fundamental Research Funds for the Central Universities
This paper proposes a new independent and related variable monitoring based on kernel principal component analysis (KPCA) and support vector data description (SVDD) algorithm. Some process variables are considered independent from other variables and the monitoring of independent and related variables should be performed separately. First, an independent variable division strategy based on mutual information is presented. Second, SVDD and KPCA methods are adopted to monitor independent variable space and related variable space, respectively. Finally, a general statistic is built according to the monitoring results of SVDD and KPCA. The proposed KPCA-SVDD method considers the related and independent characters of variables. This method combines the advantages of KPCA in managing nonlinear related variables and those of SVDD in handling independent variables. A numerical system and the Tennessee Eastman process are used to examine the efficiency of the proposed method. Simulation results have proved the superiority of KPCA-SVDD method. (C) 2016 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据