文献类型：期刊文献

英文题名：Plant-Wide Process Fine-Scale Monitoring via Distributed Static Magnitude-Dynamic Difference

作者：Song, Bing[1];Song, Yimeng[1];Jin, Yuting[1];Shi, Hongbo[1];Tao, Yang[1];Tan, Shuai[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China

年份：2023

卷号：19

期号：11

起止页码：10864

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20230813625117);WOS:【SCI-EXPANDED(收录号:WOS:001181996300023)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62073140, Grant 62073141, Grant 62103149, and Grant 62273147; in part by Shanghai Rising-Star Program under Grant 21QA1401800; and in part by the National Key Research and Development Program of China under Grant 2020YFC1522502 and Grant 2020YFC1522505. Paper no. TII-22-3669. (Corresponding author: Bing Song.)

语种：英文

外文关键词：Data driven; fault detection; plant-wide process; process monitoring

摘要：To monitor the plant-wide process finely, a novel distributed static magnitude-dynamic difference (DSM-DD) method is proposed in this article. First, given the high dimension of the collected data in the plant-wide process, the entire data space is divided into four orthogonal subspaces according to whether the data obey Gaussian distribution and whether it has serial correlation. Second, both the static magnitude and dynamic difference of the data in the four subspaces are used to build the monitoring model. In addition, not only the features within four subspaces are extracted but the correlation between different subspaces is also extracted to construct corresponding statistics. Third, all the statistics with physical significance are put together to form a statistic vector, and the local outlier factor method is used for constructing the synthetic index to determine whether the fault occurs. Finally, the superiority of the DSM-DD method is verified through a typical industrial case.