A process monitoring method for autoregressive-dynamic inner total latent structure projection  

作　　者：CHEN Yalin KONG Xiangyu LUO Jiayu 

机构地区：[1]School of Missile Engineering,Rocket Force University of Engineering,Xi’an 710025,China [2]AVIC Chengdu Caic Electronics Co.,Ltd,Chengdu 610091,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第5期1326-1336,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(62273354,61673387,61833016).

摘　　要：As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.

关 键 词：dynamic characteristic fault detection feature extraction process monitoring projection to latent structure(PLS) quality-related spatial partitioning 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]