Chemical process fault diagnosis using kernel retrofitted fuzzy genetic algorithm based learner (FGAL) with a hidden Markov model

A hybrid generative-discriminative diagnostic system based on a symbolic learner (FGAL) retrofitted with Gaussian kernel densities for generating instantaneous class probabilities which are further used by a hidden Markov model to estimate the most likely state (fault) given the past evidence is introduced for real time process fault diagnosis. The system allows symbolic knowledge extraction, it is modular and robust. The diagnostic performance of the developed system is shown on a nonisothermal cascade controlled continuously stirred tank reactor (CSTR).