Abstract
Principal Component Analysis (PCA) can be effectively used to eliminate system noise and correlation between process variables but to reserve enough original data information. Based on principal component model, performance monitoring and analysis was carried out on control system with multivariate statistical index, such as Q residuals, Hotelling T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and principal scores. This method with multivariable statistical technologies gives explicit knowledge about control system: whether the system is in-control or out-control, how disturbance and system variables influence the performance and how the control strategy work. Control system then could be evaluated with these knowledges. Application to two typical chemical processes shows the example of the method practical function.
Published Version
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