Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

Ž . Principal component analysis PCA is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and Ž . has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares DPLS has been shown to improve fault diagnosis for small-scale classification problems as compared with PCA. Fisher’s discrimiŽ . nant analysis FDA has advantages from a theoretical point of view. In this paper, we develop an information criterion that automatically determines the order of the dimensionality reduction for FDA and DPLS, and show that FDA and DPLS are more proficient than PCA for diagnosing faults, both theoretically and by applying these techniques to simulated data collected from the Tennessee Eastman chemical plant simulator. q 2000 Elsevier Science B.V. All rights reserved.