Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton

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Moussa Traore Eric Chaˆtelet Eddie Soulier Hossam A. Gabbar

Abstract

Nowadays, determining faults (or critical situations) in non- stationary environment is a challenging task in complex systems such as Nuclear center, or multi-collaboration such as crisis management. A discrete event system or a fuzzy discrete event system approach with a fuzzy role-base may re- solve the ambiguity in a fault diagnosis problem especially in the case of multiple faults (or multiple critical situations). The main advantage of fuzzy finite state automaton is that their fuzziness allows them to handle imprecise and uncertain data, which is inherent to real-world phenomena, in the form of fuzzy states and transitions. Thus, most of approaches proposed for fault diagnosis of discrete event systems require a complete and accurate model of the system to be diagnosed. However, in non-stationary environment it is hard or impossible to obtain the complete model of the system. The focus of this work is to propose an evolving fuzzy discrete event system whose an activate degree is associated to each active state and to develop a fuzzy learning diagnosis for incomplete model. Our approach use the fuzzy set of output events of the model as input events of the diagnoser and the output of a fuzzy system should be defuzzified in an appropriate way to be usable by the environment.

How to Cite

Traore, M., Chaˆtelet, E. ., Soulier, E. ., & A. Gabbar, H. . (2014). Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2353
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Keywords

discrete event system, fuzzy automaton, evolving automaton, non-stationary environment, learning diagnoser

References
Cabasino, M. P., & Alessandro Giua, C. S. (2010). Fault detection for discrete event systems using petri nets with unobservable transition. Automatica, vol. 46, pp. 1531- 1539.

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.

Cao, Y., & Ying, M. (2006). Observability and decentralized control of fuzzy discrete-event systems. IEEE Trans. Fuzzy Syst., vol. 14, pp. 202-216.

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 automato. 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.

Kwong, R. H., & Yonge-Mallo, D. L. (2011). Fault diagnosis in discrete-event systems: Incomplete models and learning. Systems, Man, and Cybernetics, Part B: Cy- bernetics, IEEE Transactions on, vol. 41 (1), pp. 118 - 130.

Liu, F., & Qiu, D. (2009a). Diagnosability of fuzzy discrete- event system: A fuzzy approach. IEE Transactions on Fuzzy Systems, vol. 17 (2), pp. 372-384.

Liu, F., & Qiu, D. (2009b). 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.

Moamar, S.-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.

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). Statesplittingandmerging- in probabilistic finite state auto mata for signal representation and analysis. 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.

Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete event systems. IEEE Transaction On Automatic
Contol, vol. 40 (9), pp. 1555-1575.
Sardouk, A., Mansouri, M., Merghem-Boulahia, L., & Gaiti,D. (2013). Crisis management using mas-based wireless sensor networks. Computer Networks, vol. 57, pp. 29-45.

Thomas, W. (1990). Handbook of theoretical computer science. Elsevier, B.

Traore, M., Moamar, S.-M., & Billaudel, P. (2013). Learn- ing diagnoser and supervision pattern in discrete event system : Application to crisis management. Annual Conference of the Prognostics and Health Management Society, ISBN-978-1-936263-06-6, New Orleans, USA, 694-701.
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Technical Papers