• principal component analysis;
  • support vector data description;
  • process monitoring;
  • just-in-time reorganizing

Although principal component analysis (PCA) is widely used for chemical process monitoring, improvements in the selection of principal components (PCs) are still needed. Given that the determination of complicated and changing fault information is not guaranteed using offline-selected PCs, this study proposes a just-in-time reorganized PCA model that objectively selects the PCs online for process monitoring. The importance of the PCs is evaluated online by kernel density estimation. The PCs indicating more varied information are then selected to reorganize the PCA model. Given that the most useful fault information is concentrated, support vector data description is used to replace traditional statistics, thereby relaxing the Gaussian assumption of the process data. The monitoring performances of the proposed method are evaluated under three cases. Compared with conventional PCA methods, more varied information is captured online, and the monitoring performances are improved. © 2014 American Institute of Chemical Engineers AIChE J, 60: 949–965, 2014