文献类型：期刊文献

英文题名：Process Monitoring via Key Principal Components and Local Information Based Weights

作者：Song, Bing[1];Zhou, Xinggui[2];Tan, Shuai[1];Shi, Hongbo[1];Zhao, Bo[1];Wang, Mengling[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：7

起止页码：15357

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20190806530087);WOS:【SCI-EXPANDED(收录号:WOS:000459411800001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703161, Grant 61673173, and Grant 61673177, in part by the Fundamental Research Funds for the Central Universities under Grant 222201714031, in part by the China Postdoctoral Research Fund under Grant 2017M611472, and in part by the Natural Science Foundation of Shanghai under Grant 16ZR1407400.

语种：英文

外文关键词：Statistical analysis; process monitoring; fault detection; principal component analysis; data analysis

摘要：There are two problems in principal component analysis (PCA), which is widely employed in multivariate statistical process monitoring. On one hand, principal components selection according to the variance of the normal training dataset cannot represent the amount of fault information included in the online data. Thus, the useful fault information loss would exist and leads to poor monitoring performance. On the other hand, although the fault information contained in every principal component is different and the principal components are treated equally in traditional PCA-based methods. Then, some useful fault information would be suppressed. In order to reduce the dimension and preserve every original variable information as complete as possible at the same time, this paper selects key principal components using our previously proposed full variable expression method. Moreover, according to the accumulated reachability distance of the online data relative to that of the offline training data, those key principal components with large accumulated reachability distance are emphasized and weighted. Finally, the statistics are constructed to monitor the operation status, and the process monitoring performance of the proposed method is evaluated under an industrial process.