文献类型：期刊文献

英文题名：Fault detection via local and nonlocal embedding

作者：Ma, Yuxin[1];Song, Bing[1];Shi, Hongbo[1];Yang, Yawei[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：2015

卷号：94

起止页码：538

外文期刊名：CHEMICAL ENGINEERING RESEARCH & DESIGN

收录：;EI(收录号：20144500173350);WOS:【SCI-EXPANDED(收录号:WOS:000350194500051)】；

基金：This research is supported by the National Nature Science Foundation of China (no. 61374140).

语种：英文

外文关键词：Fault detection; Manifold learning; Feature extraction

摘要：A novel algorithm named local and nonlocal embedding (LNLE) is proposed for fault detection of industrial processes in this paper. LNLE is a linear dimensionality reduction technique for preserving both local and global information in the training data. Aligned with the objective function of neighborhood preserving projections (NPE) which means to preserve the local data structure, a new objective function is developed to preserve the relationship between a sample and others which lie in its nonlocal area. Then, a unified optimization is constructed by minimizing the distances among neighborhood samples and maximizing the distances among nonlocal samples with an orthogonal constraint of the mapping matrix for extracting a compact representation of the original data space. Finally, the utility and feasibility of the proposed algorithm are demonstrated through a numerical example and TE benchmark process. (C) 2014 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.