Abstract
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 language | English |
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Pages (from-to) | 279-292 |
Number of pages | 14 |
Journal | Journal of Process Control |
Volume | 14 (3) |
Issue number | 3 |
DOIs | |
Publication status | Published - Aug 2004 |
ASJC Scopus subject areas
- Process Chemistry and Technology
- Control and Systems Engineering
- Industrial and Manufacturing Engineering