A multiobjective evolutionary approach to pattern recognition for robust diagnosis of process faults

The problem of robust model-based diagnosis of process faults is addressed in the framework of pattern recognition. Evolutionary algorithms of genetic type are used to solve both problems of feature selection and classifier design by means of multiobjective optimization. Process coefficients are directly identified by an on-line procedure. Symptoms are then evaluated by a non-parametric classifier. Application to a laboratory process is included. A diagnosis subsystem is designed and implemented in real-time to detect incipient faults in the components of a three-tank system. Copyright (C) 1998 IFAC.