**5.3 The offline monitoring of batches without intelligent synchronization**

For those asynchronous batches modeling and monitoring, without intelligent synchronization of DTW or OFA, the rough method of synchrozation, to prune so-called redundant data over the specified terminal or to extend the short trajectories with the last values, is experimented. All the durations of reference batches and test batches should be 3200 measurements.

Then the reference data set is arranged as a three-way **X** (*I*×*J*×*K*), where *I* corresponds to 50 batches, *J* corresponds to 9 process variables, and *K* corresponds to 3200 *th* time intervals. With the reference batch data **X**, the MPCA and MICA models are constructed initially. Offline analysis of ten test batches is executed to show if this kind of rough construction of data for MPCA or MICA is appropriate or not. After batch-wise unfolding, 8 principal components of the MPCA model are determined by the cross-validation method (Nomikos and MacGregor, 1994), which explain 82.61% of the variability in the data. 8ICs are selected for the MICA for 77.54% variation of the whole data. Fig.9 shows the results of SPE based on MPCA and MICA under 99% control limit. It is clear that neither of MPCA nor MICA does well on the incorrect asynchronous multivariate statistic model: MPCA misses the detection of the batch #2, and MICA reports false alarm batches #4,#5, and misses #1,#2.

Fig. 9. Offline analysis for ten test batches of PVC, left: MPCA, right: MICA
