文献类型：期刊文献

英文题名：Dynamic learning on the manifold with constrained time information and its application for dynamic process monitoring

作者：Yang, Jian[1];Zhang, Mingshan[1];Shi, Hongbo[1];Tan, Shuai[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：2017

卷号：167

起止页码：179

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616430904);WOS:【SCI-EXPANDED(收录号:WOS:000408790200021)】；

基金：This research is supported by the National Natural Science Foundation of China (nos. 61374140, 61673173) and Fundamental Research Funds for the Central Universities (nos. 222201717006, 222201714031).

语种：英文

外文关键词：Dynamic characteristic; Process monitoring; Time information; Serial correlation

摘要：Complex industrial processes exhibit dynamic behavior. Typically, samples are correlate in time. Therefore monitoring methods based on a single process may not perform well under such conditions. In this paper, a novel algorithm named time information constrained embedding (TICE) is proposed to improve the monitoring performance for the dynamic process. In this study, the neighbors are selected to reconstruct the current data point. With the consideration of the serial correlation, the time window with a certain length is adopted to restrict the scope of the neighbors' selection. To reveal the distance in the time scale as well as to preserve the neighborhood structure, a new expression of time weight is given to quantify the importance of sequential neighbors. Furthermore, an enhanced objective function is constructed to calculate the transformation matrix. Finally, the superiority of the proposed method is illustrated by an application example (TecQuipment CE117 process trainer) and the Tennessee Eastman (TE) process.