Isolation enhanced principal component analysis

Principal component analysis (PCA) may reduce the dimensionality of plant m odels significantly by exposing linear dependences among the variables. Whi le PCA is a popular tool in detecting faults in complex plants, it offers l ittle support in its original form for fault isolation. However, by utilizi ng the equivalence between PCA and parity relations, all the powerful conce pts of analytical redundancy may be transferred to PCA. Following this path , it is shown how structured residuals, which have the same isolation prope rties as analytical redundancy residuals, are obtained by PCA. The existenc e conditions of such residuals are demonstrated, as well as how disturbance decoupling is implied in the method. The effect of the presence of control constraints in the training data is analyzed. Statistical testing methods for structured PCA residuals are also outlined. The theoretical findings ar e fully supported by simulation studies performed on the Tennessee Eastman process.