Skip to Main Content
The traditional MPCA model takes the entire batch data as a single object, and it is difficult to reveal the changes of process correlation from stage to stage. Considering that multiple phases with transitions from phase to phase are important characteristics of many batch processes, it is desirable to develop stage-based models. However, some stage-based monitoring methods may occur false alarm and missing alarm at the beginning and end of each stage, because the hard-partition and misclassification problems. To overcome the above matters flexibly, a novel multiple PCA batch monitoring approach using fuzzy clustering soft-transition is proposed. It reduces the false alarm and missing alarm for batch process in on-line monitoring due to batch variation. The proposed method is applied to detect and identify faults in the well-known simulation benchmark of fed-batch penicillin production. The simulation results demonstrate the effectiveness and feasibility of the proposed method, which detects various faults more promptly with desirable reliability.