In chemical process

a large number of measured andmanipulated variables are highly correlated. Principal component analysis (PCA) iswidely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However

it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed

but the information is always shortage and deficient in practice. Therefore

this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change

namely to grasp the intrinsic local information underlying the process changes

but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect. The method is demonstrated in TE process

and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.