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

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外文摘要：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.

外文关键词：

dynamic characteristicfault detectionfeature extractionprocess monitoringprojection to latent structure(PLS)quality-relatedspatial partitioning

作者：

CHEN Yalin、KONG Xiangyu、LUO Jiayu

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作者单位：

School of Missile Engineering,Rocket Force University of Engineering,Xi'an 710025,China

AVIC Chengdu Caic Electronics Co.,Ltd,Chengdu 610091,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

项目编号：

622733546167338761833016

出版年：

2024

DOI：

10.23919/JSEE.2024.000105

系统工程与电子技术(英文版)

中国航天科工防御技术研究院中国宇航学会中国系统工程学会中国系统仿真学会

系统工程与电子技术(英文版)

CSTPCD

影响因子：0.64

ISSN：1004-4132

年,卷(期)：2024.35(5)