Improved fault detection in nonlinear chemical processes using WKPCA-SVDD

Conventional kernel principal component analysis (KPCA) does not always perform well for nonlinear process monitoring because the beneficial information for fault detection may be submerged under the retained kernel principal components (KPCs). To overcome this deficiency, an adaptively weighted KPCA integrated with support vector data description (WKPCA-SVDD) monitoring method is proposed. In WKPCA-SVDD, the importance of each KPC is evaluated online by the change rate of T2 statistic and then distinguished weighting values are set on the KPCs. The behaviors of all KPCs are comprehensively evaluated by the SVDD technique. Since the beneficial information is highlighted, the monitoring performance of the statistic in the dominant subspace can be improved. The proposed WKPCA-SVDD is applied to both a numerical process and the complicated Tennessee Eastman benchmark process. Monitoring results have indicated the efficiency of the WKPCA-SVDD method.