文献类型：期刊文献

英文题名：Quality Relevant and Independent Two Block Monitoring Based on Mutual Information and KPCA

作者：Huang, Junping[1];Yan, Xuefeng[1]

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

年份：2017

卷号：64

期号：8

起止页码：6518

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录：;EI(收录号：20173804195961);WOS:【SCI-EXPANDED(收录号:WOS:000405172300047)】；

基金：This work was supported in part by the 973 Project of China under Grant 2013CB733600 and in part by the National Natural Science Foundation of China under Grant 21176073.

语种：英文

外文关键词：Kernel principal component analysis (KPCA); mutual information (MI); nonlinear process monitoring; quality-independent monitoring; quality-relevant monitoring

摘要：Traditional process monitoring methods take all the measured variables into account, whereas it will be inappropriate for indicating quality-relevant faults. Somemeasured variables are independent from the quality variables and these redundancy variables will no doubt degrade the prediction performance of quality variables. This paper proposes a novel quality relevant and independent two block monitoring scheme based on mutual information (MI) and kernel principal component analysis (KPCA). First, all the process variables are divided into two subblocks according to their MI value with quality variables. Then, KPCA monitors the quality-relevant subblock and quality-independent subblock, respectively. When a fault is detected, kernel principal component regression is further utilized to obtain the predicted state of quality variables. Either of the information, whether the current fault disturbs quality-relevant variables or process quality, is necessary and important for engineers. The benefits of MI-KPCA are illustrated through a numerical simulation and the Tennessee Eastman process, and the results reveal the superiority of the proposal compared with some other monitoring methods.