文献类型：期刊文献

中文题名：利用挑选关键主元以改善过程监控性能的方法研究（英文）

英文题名：Improved performance of process monitoring based on selection of key principal components

作者：宋冰[1];马玉鑫[1];侍洪波[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：23

期号：12

起止页码：1951

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;EI(收录号：20155101695351);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000367537400004)】；CSCD:【CSCD2015_2016】；

基金：Supported by the National Natural Science Foundation of China (No. 61374140) and Shanghai Pujiang Program (Project No. 12PJ1402200)

语种：中文

中文关键词：性能改进;过程监控;主成分分析法;分选;数据空间;监测统计;信息丢失;变量表达式

外文关键词：Principal component analysis Information loss Fault detection Key principal component

摘要：Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.
Conventional principal component analysis（PCA） can obtain low-dimensional representations of original data space, but the selection of principal components（PCs） based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.