Information Criterion for Determination Time Window Length of Dynamic PCA for Process Monitoring

Xiuxi LI, Yu QIAN*, Junfeng WANG, S Joe QIN

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

Abstract

Principal component analysis (PCA) is based on and suitable to analysis of stationary processes. When it is applied to dynamic process monitoring, the moving time window approach is used to construct the data matrix to be analyzed. However, the length of the time window and the moving width between time widows are often empirically tested and selected. In this paper, a criterion for determining the time window length is proposed for dynamic process monitoring. A new algorithm of dynamic monitoring is then presented. The proposed selection criterion is used in the new algorithm. Finally, the proposed approach is successfully applied to a two-input two-output process and Tennessee Eastman process for dynamic monitoring. © 2003 Elsevier B.V. All rights reserved.
Original languageEnglish
Title of host publicationProceedings of the 36th European Symposium of the Working Party on Computer Aided Process Engineering (ESCAPE), 1-4 June 2003, Lappeenranta, Finland
EditorsAndrzej KRASLAWSKI, Ilkka TURUNEN
PublisherElsevier
Pages461-466
Number of pages6
ISBN (Print)9780444513687
DOIs
Publication statusPublished - Jun 2003
Externally publishedYes
Event13th European Symposium on Computer Aided Process Engineering (ESCAPE-13) - Lappeenranta, Finland
Duration: 1 Jun 20034 Jun 2003

Publication series

NameComputer Aided Chemical Engineering
PublisherElsevier
Volume14
ISSN (Print)1570-7946

Conference

Conference13th European Symposium on Computer Aided Process Engineering (ESCAPE-13)
Country/TerritoryFinland
CityLappeenranta
Period1/06/034/06/03

Bibliographical note

Financial support from the National Natural Science Foundation of China (No. 29976015), the China Excellent Young Scientist Fund, China Major Basic Research Development Program (G20000263) are gratefully acknowledged.

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