Dynamic Multivariate Statistical Process Control using Subspace Identification

R.J. Treasure, Uwe Kruger, J.E. Cooper

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)


This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.
Original languageEnglish
Pages (from-to)279-292
Number of pages14
JournalJournal of Process Control
Volume14 (3)
Issue number3
Publication statusPublished - Aug 2004

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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