Applying State Space to SPC: Monitoring Multivariate Time Series

By Xia Pan, Jeffrey E. Jarrett. 2004

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Monitoring cross-sectional and serially interdependent processes has become a new issue in statistical process control (SPC). In up-to-date SPC literature, Kalman filtering was reported to monitor univariate autocorrelated processes. This paper applies a Kalman filter or state-space method for SPC to monitoring multivariate time series. We use Aoki's approach to estimate the parameter matrices of a state-space model. Multivariate Hotelling T 2 control charts are employed to monitor the residuals of the state-space. Examples of this approach are illustrated.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/53f493f970155e24bce2556d15c64fca54ce1efe