Fault detection and classification for a two-stage batch process
Multilway principal component analysis (MPCA) has been extensively applied to batch process monitoring. In the case of monitoring a two-stage batch process, the inter-stage variation is neglected if MPCA models for each individual stage are used. On the other hand, if two stages of reference data are combined into a large dataset that MPCA is applied to, the dimensions of the unfolded matrix will increase dramatically. In addition, when an abnormal event is detected, it is difficult to identify which stage's operation induced this alarm. In this paper, partial least squares (PLS) is applied to monitor the inter-stage relation of a two-stage batch process. In post-analysis of abnormalities, PLS can clarify whether root causes are from previous stage operations or due to the changes of the inter-stage correlations. This approach was successfully applied to a semiconductor manufacturing process. Copyright (C) 2008 John Wiley & Sons, Ltd.
principal component analysis batch process monitoring partial least squares consensus pca hierarchical pca canonical variate analysis