Abstract
Principal component analysis (PCA) method used in the fault diagnosis of industrial process is the most famous one of multivariate statistical methods. It concerns using few linear combinations of the set of process variables to explain the whole process operation state, and to detect the process fault. In this paper, the PCA method has been applied in Tennessee Eastman (TE) chemical process model. The simulation results show that PCA method can detect the fault quickly and effectively in some complex nonlinear chemical process.
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