PLS-based Similarity Analysis for Mode Identification in Multimode Manufacturing Processes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)777-782
Journal / PublicationIFAC-PapersOnLine
Volume28
Issue number8
Online published25 Sep 2015
Publication statusPublished - 2015
Externally publishedYes

Conference

Title9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015
PlaceCanada
CityWhistler
Period7 - 10 June 2015

Abstract

Many industrial manufacturing processes have multiple operation modes because of different strategy and varying feedstock. The traditional statistical process monitoring tools such as PCA and PLS cannot be applied since they assume that the process must have single mode operation region only. In this paper, all the factors that will affect the change of the mode are considered, a similarity factor including the similarity factor of PLS models and the mean shift of the external variables is introduced to measure the similarity of two sets of data. On basis of this similarity factor, a moving window is used and a mode identification approach for multimode process monitoring is proposed. The proposed approach is demonstrated on the benchmark Tennessee Eastman process.

Research Area(s)

  • External analysis, Mode identification, Multiple operation mode, Partial least square(PLS), Process monitoring

Citation Format(s)

PLS-based Similarity Analysis for Mode Identification in Multimode Manufacturing Processes. / Zheng, Ying; Qin, S. Joe; Wang, Fu-Li.

In: IFAC-PapersOnLine, Vol. 28, No. 8, 2015, p. 777-782.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review