Sequential Control Performance Diagnosis of Steel Processes

Abstract

Abstract A sequential method for Control Performance Diagnosis using a classification tree to predict possible root-causes of poor performance is presented. The classification tree methodology is used to combine process pre-assessment (nonlinearities detection, delays estimation and controller assessment), control performance assessment (CPA) and analysis of variance (ANOVA) into an integrated framework. An initial process data set is analysed and the results are used as decision thresholds for the classification tree. The methodology is capable to identify root-causes such as poor tuning, inadequate control structure, nonlinearities, process mismatch and disturbance changes. The proposed methodology is applied to individual loops of a tandem cold rolling mill.