1384367

11630

Partial PCA based on principal component analysis (PCA) with ideas borrowed from parity relations is a useful method in fault isolation (J. Gertler, W. Li, Y. Huang, T.J. McAvoy, Isolation enhanced principal component analysis, AIChE Journal 45(2) (1999) 323±334). By performing PCA on subsets of variables, a set of structured residuals can be obtained in the same way as structured parity relations. The structured residuals are utilized in composing an isolation scheme for sensor and actuator faults, according to a properly designed incidence matrix. To overcome the limitations of PCA, nonlinear approaches based on generalized PCA (GPCA) and non- linear PCA (NPCA) are proposed. The nonlinear methods are demonstrated on an arti®cial 2 2 system while simulation studies on the Tennessee Eastman process illustrate the linear method and some extensions.

459

fault detection and isolation , structured residuals , Nonlinear models , Partial PCA , Nonlinear PCA

Studia Iranica

469

469