Evolution of multivariate statistical process control: application of independent component analysis and external analysis

Abstract

Univariate and multivariate statistical process control (USPC and MSPC) methods have been widely used in process industries for fault detection. However, their practicability and achievable performance are limited due to the assumptions that a continuous process is operated in a particular steady state and that variables are normally distributed. In the present work, external analysis is proposed to distinguish faults from normal changes in operating conditions. In addition, to further improve the monitoring performance, a new MSPC method based on independent component analysis (ICA) is used. The simulation results of a linear multivariable system and a CSTR process have clearly shown the superiority of ICA-based SPC over USPC and PCA-based SPC, and also the usefulness of the proposed external analysis.