Abstract
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).
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References
Bezdek, J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981).
Leonard, J. A. and M. A. Kramer. Diagnosing dynamic faults using modular neural nets. IEEE Expert,8 (2), 44–53 (1993).
Özyurt, I. Burak. Chemical Process Fault Diagnosis using pattern recognition and semi-quantitative model based methods. Ph.D. Dissertation in Engineering Science, University of South Florida (1998).
Özyurt, I. Burak and Aydin Sunol. Fuzzy genetic algorithm based inductive learning system (FGALS): a new machine learning approach and application for chemical process fault diagnosis. in Applications of AI in Engineering XI, Computational Mechanics Publications (1996).
Rabiner, L. and B. H. Juang., Fundamentals of speech recognition. Prentice Hall, New Jersey (1993).
Silverman, B. Density estimation for statistics and data analysis. Chapman and Hall, New York (1986).
Smyth, P. Hidden Markov models for fault detection in dynamic systems. Pattern Recog., 27, 149–164 (1994).
Smyth, P., Probability density estimation and local basis function neural networks. in S. H. Hanson, T. Petsche, M. Kearns and R. Rivest (editors) Computational learning theory and natural learning systems Volume II: Intersections between theory and experiment MIT Press, Massachusetts, 233–248 (1994b).
Smyth, P, A. Gray and U. M. Fayyad. Retrofitting decision tree classifiers using kernel density estimation. Proceedings of the 12th International Conference on Machine Learning, CA (1995).
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© 1998 Springer-Verlag
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Özyurt, I.B., Sunol, A.K., Hall, L.O. (1998). Chemical process fault diagnosis using kernel retrofitted fuzzy genetic algorithm based learner (FGAL) with a hidden Markov model. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_748
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DOI: https://doi.org/10.1007/3-540-64582-9_748
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