A UNIFIED GEOMETRIC APPROACH TO PROCESS AND SENSOR FAULT IDENTIFICATION AND RECONSTRUCTION - THE UNIDIMENSIONAL FAULT CASE

Fault detection and process monitoring using principal component analy sis (PCA) have been studied intensively and applied to industrial proc esses. This paper addresses some fundamental issues in detecting and i dentifying faults. We give conditions for detectability, reconstructab ility, and identifiability of faults described by fault direction vect ors. Such vectors can represent process as well as sensor faults using a unified geometric approach. Measurement reconstruction is used for fault identification, and consists of sliding the sample vector toward s the PCA model along the fault direction. An unreconstructed variance is defined and used to determine the number of principal components f or best fault identification and reconstruction. The proposed approach is demonstrated with data from a simulated process plant. Future dire ctions on how to incorporate dynamics and multidimensional faults are discussed. (C) 1998 Elsevier Science Ltd. All rights reserved.