Fault isolation in nonlinear systems with structured partial principal component analysis and clustering analysis

Partial principal component analysis (PCA) and parity relations are proven to be useful methods in fault isolation. To overcome the limitation of appl ying partial PCA to nonlinear problems, a new approach utilizing clustering analysis is proposed. By dividing a partial data set into smaller subsets, one can build more accurate PCA models with fewer principal components, an d isolate faults with higher precision. Simulations on a 2 x 2 nonlinear sy stem and the Tennessee Eastman (TE) process show the advantages of using th e clustered partial PCA method over other nonlinear approaches.