文献类型：期刊文献

英文题名：Time-space locality preserving coordination for multimode process monitoring

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

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

年份：2016

卷号：151

起止页码：190

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20240915627645);WOS:【SCI-EXPANDED(收录号:WOS:000370884700023)】；

基金：This research is supported by the National Natural Science Foundation of China (nos. 61374140 and 61403072).

语种：英文

外文关键词：Multimode process monitoring; Time-space locality preserving coordination; Internal representation; Posterior probability; Global model

摘要：In this paper, an original algorithm called time-space locality preserving coordination (TSLPC) is put forward to monitor multimode processes. First, a block-wise matrix with multimodality labels is defined for dealing with the multimode problem. The information of internal representations and posterior probabilities is contained in the defined block-wise matrix. In order to make the posterior probabilities of data belonging to each mode more accurate, a novel distance applying the neighborhood information is developed. Next, considering the within-mode serial correlation, not only nearest data based on the distance but also adjacent data based on the time are selected as neighbors in the proposed TSLPC algorithm. Then, the low-dimensional global characterization of the defined block-wise matrix can be acquired by optimally preserving the neighborhood structure of the dataset. In addition, the proposed TSLPC algorithm establishes a global model for multimode processes on the basis of the defined block-wise matrix, which is different from traditional multimode process monitoring methods where multiple local models corresponding to multiple modes are constructed. Finally, the multimode continuous stirred tank reactor (CSTR) process and the multimode Tennessee Eastman (TE) process are studied to show the advantage of the proposed TSLPC method. (C) 2016 Elsevier B.V. All rights reserved.