Fault Diagnosis in Fuzzy Discrete Event System: IncompleteModels and Learning
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Published
Nov 1, 2020
M. Traore
Eric Chˆatelet
Eddie Soulier
Hossam A. Gabbar
Abstract
Nowadays, determining faults in non-stationary environment and that can deal with the problems of fuzziness impreciseness and subjectivity is a challenging task in complex systems such as nuclear center, or wind turbines, etc. Our objective in this paper is to develop models based on fuzzy finite state automaton with fuzzy variables describing the industrial process in order to detect anomalies in real time and possibly in anticipation. A diagnosis method has for goal to alert actors responsible for managing operations and resources, able to adapt to the emergence of new procedures or improvisation in the case of unexpected situations. The diagnoser module use the outputs events and membership values of each active state of the model as input events.
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Keywords
diagnosis, prediction, Crisis Management, fuzzy automaton
References
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Doostfatemeh, M., & Kremer, S. C. (2005). New directions in fuzzy automaton. International Journal of Approximate Reasoning, vol. 38, pp. 175-214.
Dzelme-Berzina, I. (2009). Mathematical logic and quantum finite state automata. Theoretical Computer Science, vol. 410, pp. 1952-1959.
Gerasimos, G. R. (2009). Fault detection and isolation based on fuzzy automaton. Information Sciences, vol. 179, pp. 1893-1902.
Gonzalo, B., & Gracin, T. (2010). Pattern recognition using temporal fuzzy automata. Fuzzy Sets and Systems, vol. 161 (1), pp. 37-55.
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Kwong, R. H., & Yonge-Mallo, D. L. (2011). Fault diagnosis in discrete-event systems: Incomplete models and learning. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 41 (1), pp. 118 -130.
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Patela, A. M., & Joshi, A. Y. (2013). Modeling and analysis of a manufacturing system with deadlocks to generate the reachability tree using petri net system. International Conference On DESIGN AND MANUFACTURING, IConDM 2013, vol. 64, pp. 775-784.
Rigatos, G. G. (2009). Fault detection and isolation based on fuzzy automata. Information Sciences, vol. 179, pp. 1893-1902.
Sayed-Mouchaweh, M., & Billaudel, P. (2012). Abrupt and drift-like fault diagnosis of concurent discrete event systems. Machine Learning and Applications (ICMLA), vol. 2, pp. 434-439.
Thomas, W. (1990). Handbook of theoretical computer science. Elsevier, B. Traore,M., Chatelet, E., Soulier, E., & Gabbar, H. A. (2014). Learning diagnosis based on evolving fuzzy finite state automaton. Annual Conference of the Prognostics and Health Management Society, pp. 41-50.
Traore, M., Sayed-Mouchaweh, M., & Billaudel, P. (2014). Adaptive supervision pattern in discrete event systems: Application to crisis management. International Journal of Prognostics and Health Management (IJPHM).
Wang, S., Ji, Y., Dong, W., & Sun, X. (2013). A novel fault diagnosis framework of computing with words based on lattice-valued fuzzy automata. Conference on Measuring Technology and Mechatronics Automation, pp. 62-66.
Cao, Y., & Ying, M. (2005). Supervisory control of fuzzy discrete event systems. IEEE transactions on systems, Man, and Cybernetics-part B: Cybernetics, vol. 35 (2), pp. 366-370.
Cassandras, C. G., & Lafortune, S. (1999). Introduction to discrete event systems. Boston, MA:Kluwer.
Doostfatemeh,M., & Kremer, S. C. (2004). The significance of output mapping in fuzzy automaton. Proceedings of the 21th Iranian Conference on Electrical Engineering (ICEE).
Doostfatemeh, M., & Kremer, S. C. (2005). New directions in fuzzy automaton. International Journal of Approximate Reasoning, vol. 38, pp. 175-214.
Dzelme-Berzina, I. (2009). Mathematical logic and quantum finite state automata. Theoretical Computer Science, vol. 410, pp. 1952-1959.
Gerasimos, G. R. (2009). Fault detection and isolation based on fuzzy automaton. Information Sciences, vol. 179, pp. 1893-1902.
Gonzalo, B., & Gracin, T. (2010). Pattern recognition using temporal fuzzy automata. Fuzzy Sets and Systems, vol. 161 (1), pp. 37-55.
Kilic, E. (2008). Diagnosability of fuzzy discrete event systems. Information Sciences, vol. 178, pp. 858-870.
Kwong, R. H., & Yonge-Mallo, D. L. (2011). Fault diagnosis in discrete-event systems: Incomplete models and learning. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 41 (1), pp. 118 -130.
Lin, F., & Ying, H. (2002). Modeling and control of fuzzy discrete event systems. IEEE Transactions on systems, man, and Cybernetics-part B: Cybernetics, pp. 408- 414.
Liu, F., & Qiu, D. (2009). Diagnosability of fuzzy discrete event systems: A fuzzy approach. IEEE Transactions on fuzzy system, vol. 17 (2), pp. 372-384.
Luo, M., Li, Y., Sun, F., & Liu, H. (2012). A new algorithm for testing diagnosability of fuzzy discrete event systems. Information Sciences, vol. 185, pp. 100 - 113.
Moghari, S., Zahedi,M.M., & Ameri, R. (2011). New direction in fuzzy tree automata. Iranian Journal of Fuzzy Systems, vol. 8 (5), pp. 59-68.
Mukherjee, K., & Ray, A. (2014). Statesplittingandmerginginprobabilisticfinitestate automataforsignalrepresentationandanalysis. Signal Processing, vol. 104, pp. 105-119.
Patela, A. M., & Joshi, A. Y. (2013). Modeling and analysis of a manufacturing system with deadlocks to generate the reachability tree using petri net system. International Conference On DESIGN AND MANUFACTURING, IConDM 2013, vol. 64, pp. 775-784.
Rigatos, G. G. (2009). Fault detection and isolation based on fuzzy automata. Information Sciences, vol. 179, pp. 1893-1902.
Sayed-Mouchaweh, M., & Billaudel, P. (2012). Abrupt and drift-like fault diagnosis of concurent discrete event systems. Machine Learning and Applications (ICMLA), vol. 2, pp. 434-439.
Thomas, W. (1990). Handbook of theoretical computer science. Elsevier, B. Traore,M., Chatelet, E., Soulier, E., & Gabbar, H. A. (2014). Learning diagnosis based on evolving fuzzy finite state automaton. Annual Conference of the Prognostics and Health Management Society, pp. 41-50.
Traore, M., Sayed-Mouchaweh, M., & Billaudel, P. (2014). Adaptive supervision pattern in discrete event systems: Application to crisis management. International Journal of Prognostics and Health Management (IJPHM).
Wang, S., Ji, Y., Dong, W., & Sun, X. (2013). A novel fault diagnosis framework of computing with words based on lattice-valued fuzzy automata. Conference on Measuring Technology and Mechatronics Automation, pp. 62-66.
Section
Technical Papers