文献类型：期刊文献

英文题名：A Distributed Adaptive Monitoring Method for Performance Indicator in Large-Scale Dynamic Process

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

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

年份：2023

卷号：19

期号：10

起止页码：10425

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20230813623585);WOS:【SCI-EXPANDED(收录号:WOS:001047436000043)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62103149, Grant 62073140, and Grant 62073141, in part by Shanghai Chenguang Project under Grant 21CGA37, in part by Shanghai Rising-Star Program under Grant 21QA1401800, in part by the National Key Research and Development Program of China under Grant 2020YFC1522502 and Grant 2020YFC1522505. Paper no. TII-22-3227. (Corresponding author: Hongbo Shi.)

语种：英文

外文关键词：Distributed adaptive principal component regression; fault detection; multivariate statistics; performance indicator; process monitoring

摘要：The dynamic time-varying characteristic has brought great challenges to the plant-wide process monitoring. In this article, a distributed adaptive principal component regression algorithm is proposed for the online indicator monitoring of large-scale dynamic process. First, the distributed data subblocks are constructed according to the process operation units. In each subblock, an adaptive resampling method based on the subblock data and plant-wide data is presented to construct the modeling sample sets, which can extract the process local and global information simultaneously. Afterwards, the indicator-related feature is extracted, and the Bayesian method is used to integrate the subblock monitoring results. Through the collaborative monitoring of the process local and global feature spaces, a refined monitoring decision can be obtained. Finally, a numerical example and Tennessee Eastman process are used to illustrate the effectiveness of the proposed method.