文献类型：期刊文献

英文题名：Distributed partial least squares based residual generation for statistical process monitoring

作者：Tong, Chudong[1];Lan, Ting[1];Yu, Haizhen[1];Peng, Xin[2]

机构：[1]Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Zhejiang, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Hefei, Anhui, Peoples R China

年份：2019

卷号：75

起止页码：77

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20190406423318);WOS:【SCI-EXPANDED(收录号:WOS:000463983000007)】；

基金：The financial supports from the National Natural Science Foundation of China (61503204 & 61773225), the Natural Science Foundation of Zhejiang Province (LY16F030001), K.C.Wong Magna Fund in Ningbo University, the Fundamental Research Funds for the Central Universities under Grant 222201817006, and the Alexander von Humboldt Foundation are greatly acknowledged.

语种：英文

外文关键词：Residual generation; Partial least squares; Principal component analysis; Statistical process monitoring

摘要：The main focus of the current work is to propose a purely data-based residual generation method for statistical process monitoring. The proposed approach utilizes but not limit to the partial least squares (PLS) algorithm to construct a specific regression model for each variable in a distributed manner, the model residual (i.e., estimation error) instead of the original data and the PLS latent components is then monitored. Given that every variable is transferred into the residual through its corresponding soft sensing model, the generated residual can reflect the variation in the defined input-output relationship. Furthermore, the residual is expected to follow a Gaussian distribution or at least much closer to a Gaussian distribution in contrast to the original data and the latent components, once the output variable is well predicted by the regression model. The main contributions of the presented work are as follows: 1) distributed soft sensing models for generating residuals, 2) statistical process monitoring for the generated residuals instead of original data, and 3) the comparison studies demonstrate the validity and superiority of the proposed monitoring scheme with the utilization of the PLS algorithm. It can be concluded from the comparisons and the illustrated superiority that the proposed approach would be an efficient and comparative alternative in process monitoring. (C) 2019 Elsevier Ltd. All rights reserved.