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

Principal component analysis (PCA) is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical proces ses. Although PCA contains certain optimality properties in terms of fault detection, and has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares (DPLS) has b een shown to improve fault diagnosis for small-scale classification problem s as compared with PCA. Fisher's discriminant 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 re duction for FDA and DPLS, and show that FDA and DPLS are more proficient th an PCA for diagnosing faults, both theoretically and by applying these tech niques to simulated data collected from the Tennessee Eastman chemical plan t simulator. (C) 2000 Elsevier Science]B.V. All rights reserved.