文献类型：期刊文献

英文题名：Key principal components with recursive local outlier factor for multimode chemical process monitoring

作者：Song, Bing[1];Tan, Shuai[1];Shi, Hongbo[1]

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

年份：2016

卷号：47

起止页码：136

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20163902845835);WOS:【SCI-EXPANDED(收录号:WOS:000388054500012)】；

基金：This research is supported by the National Natural Science Foundation of China (No. 61374140) and the National Natural Science Foundation of China (No. 61403072)

语种：英文

外文关键词：Multimode process monitoring; Principal component analysis; Recursive local outlier factor; Cumulative percent expression; Key principal components

摘要：Owing to various manufacturing strategies and demands of markets, chemical processes often involve multiple operating modes. How to identify mode from multimode process data collected under both stable and transitional modes is an important issue. This paper proposes a novel mode identification algorithm-recursive local outlier factor (RLOF) based on the sequential information in the time scale and the density information in the spatial scale. In this algorithm, not only the number of modes does not need to be determined in advance, but also details of mode switching can be acquired. In addition, the principal components (PCs) chosen by the variance of overall dataset in principal component analysis (PCA) cannot guarantee that all variables express information as completely as possible. Using the defined cumulative percent expression (CPE), this study chooses key PCs (KPCs) according to each variable. Moreover, fault diagnosis is realized via the contribution of every variable to key PCs. Finally, the monitoring performance is evaluated under the Tennessee Eastman (TE) benchmark and the continuous stirred tank reactor (CSTR) process. (C) 2016 Elsevier Ltd. All rights reserved.