Abstract
To on-line forecast faults in batch processes, we introduced integrated models combining MPCA and ARMA. Time series of T2 and SPE statistics obtained from MPCA models based on variable cross-correlations are employed to build univariate ARMA models in terms of their auto-correlations, predicting the h-step-ahead values implying process dynamic behaviors. The integrated models not only take advantage of both powerful data compression and prominent prediction ability, but also improve the performance of MPCA based process monitoring as well as trend forecasting, which are of great significance to ensure safe and smooth running of batch processes. Finally, experimental studies consisting in fed-batch penicillin benchmark problems demonstrate the effectiveness and potentials of the contributions.
Published Version
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