文献类型：期刊文献

英文题名：Slow feature analysis based on online feature reordering and feature selection for dynamic chemical process monitoring

作者：Huang, Jian[1,2];Ersoy, Okan K.[2];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;[2]Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA

年份：2017

卷号：169

起止页码：1

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616343018);WOS:【SCI-EXPANDED(收录号:WOS:000413127100001)】；

基金：The authors are grateful for the support of the 973 Project of China (2013CB733600), and Fundamental Research Funds for the Central Universities under Grant of China (222201717006).

语种：英文

外文关键词：Slow feature analysis; Online feature reordering; Process monitoring

摘要：This study considers the insufficiency of traditional monitoring methods to eliminate dynamics, and proposes a novel online feature reordering- and feature selection-based slow feature analysis (SFA) algorithm. The SFA algorithm explores the process dynamics from the view of inner variation of data to extract the slowly varying features. The extracted SFs are considered as the representations of steady- and dynamic-state processes. Online feature reordering and feature selection strategies maximize online fault information and can be used to perform fault detection operation. The proposed method is applied to two simulated processes. Monitoring results show that the proposed method has better monitoring results than those of traditional methods.