文献类型：期刊文献

英文题名：Dynamic graph embedding for fault detection

作者：Zhao, Haitao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2018

卷号：117

起止页码：359

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20183005584275);WOS:【SCI-EXPANDED(收录号:WOS:000441891600028)】；

基金：This research is sponsored by National Natural Science Foundation of China (61375007) and Basic Research Programs of Science and Technology Commission Foundation of Shanghai (15JC1400600).

语种：英文

外文关键词：Process monitoring; Fault detection; Dimension reduction; Dynamic feature extraction

摘要：Using sequence information can improve performances in fault detection for serial (temporal) correlated process data. Classical methods firstly construct extended vectors through concatenating current process data and a certain number of previous process data, and then take dimension reduction methods. However, the simple extension of process data may distort the correlation between variables and largely increase the dimensionality. This paper proposes a novel algorithm, called Dynamic Graph Embedding (DGE), for fault detection. DGE adopts augmented matrices instead of extended vectors to encode sequence information. Furthermore, DGE incorporates both time information and neighborhood information to form similarities of different process data. And then DGE is designed to obtain embedding matrices with Markov chain analysis of the similarities. Extensive experimental results on the Tennessee Eastman (TE) benchmark process show the superiority of DGE in terms of missed detection rate (MDR) and false alarm rate (FAR). (C) 2018 Elsevier Ltd. All rights reserved.