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Decentralized prognosis of fuzzy discrete-event systems | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 11، دوره 16، شماره 3، مرداد و شهریور 2019، صفحه 127-143 اصل مقاله (851.47 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2019.4650 | ||
| نویسندگان | ||
| B. Benmessahel* 1؛ M. Touahria1؛ F. Nouioua2؛ J. Gaber3؛ P. Lorenz4 | ||
| 1Computer Science Department, University of Ferhat Abbas Setif 1, Pole 2 - El Bez, 19000 Setif, Algeria | ||
| 2Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296, Marseille, France | ||
| 3Computer Science Department, Universite de Technologie de Belfort Montbeliard, Rue Thierry Mieg, 90010 Belfort, France | ||
| 4Computer Science Department, IUT, University of Haute Alsace, Colmar, France | ||
| چکیده | ||
| This paper gives a decentralized approach to the problem of failure prognosis in the framework of fuzzy discrete event systems (FDES). A notion of co-predictability is formalized for decentralized prognosis of FDESs, where several local agents with fuzzy observability rather than crisp observability are used in the prognosis task. An FDES is said to be co-predictable if each faulty event can be predicted prior to its occurrence by at least one local agent using the observability of fuzzy events. The verification of the decentralized predictability is performed by constructing a fuzzy co-verifier from a given FDES. The complexity of the fuzzy co-verifier is polynomial with respect to the FDES being predicted, and is exponential with respect to the number of the local prognosis agents. Then, a necessary and sufficient condition for the co-predictability of FDESs is given. In addition, we show that the proposed method may be used to deal with the decentralized prognosis for both FDESs and crisp DESs. Finally, to illustrate the effectiveness of the approach, some examples are provided. | ||
| کلیدواژه‌ها | ||
| Co-predictability؛ Discrete-event systems؛ Decentralized prognosis؛ Failure detection؛ Fuzzy automata؛ Fuzzy systems | ||
| مراجع | ||
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[1] Y. Z. Cao, M. S. Ying, Supervisory control of fuzzy discrete event systems, IEEE Transactions on Systems, Man, and Cybernetics, 35 (2005), 366-371. [2] Y. Cao, M. Ying, Observability and Decentralized Control of Fuzzy Discrete Event Systems, Ieee Transactions on Automatic Control, 14(2) (2006), 202-216. [3] F. Cassez, A. Grastien, Predictability of event occurrences in timed systems, Proceedings Springer Conference on Formal Modeling and Analysis of Timed Systems, 8053 (2013), 62-76. [4] C. G. Cassandras, S. Lafortune, Introduction to Discrete Event Systems, Boston: Springer-Verlag, 1999. [5] M. Chang, W. Dong, Y. Ji, L. Tong, On fault predictability in stochastic discrete event systems, Asian Journal of Control, 15(5) (2013), 1458-1467. [6] J. Chen, R. Kumar, Stochastic failure prognosability of discrete event systems, IEEE Transactions on Automatic Control, 60(6) (2015), 1570-1581. [7] S. Genc, S. Lafortune, Predictability of event occurrences in partially observed discrete-event systems, Automatica, 45(2) (2009), 301-311. [8] R. Huq, G. K. I. Mann, R. G. Gosine, Distributed fuzzy discrete event system for robotic sensory information processing, Expert Systems, 23(5) (2006), 273-289. [9] R. Huq, G. K. I. Mann, R. G. Gosine, Behavior-modulation technique in mobile robotics using fuzzy discrete event system, IEEE Transactions on Robotics, 22(5) (2006), 903-916. [10] A. Jayasiri, G. K. Mann, R. G. Gosine, Behavior coordination of mobile robotics using supervisory control of fuzzy discrete event systems, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 41 (2011), 1224-1238. [11] T. Jeron, H. Marchand, S. Genc, S. Lafortune, Predictability of sequence patterns in discrete event systems, Proc of IFAC World Congress, (2008), 537-543. [12] A. Khoumsi, H. Chakib, Conjunctive and Disjunctive Architectures for Decentralized Prognosis of Failures in Discrete-Event Systems, IEEE Transactions on Automation Science and Engineering, 9(2) (2012), 412-417. [13] A. Khoumsi, H. Chakib, Multi-decision decentralized prognosis of failures in discrete event systems, American Control Conference, (2009), 4974-4981. [14] E. Kilic, Diagnosability of fuzzy discrete event systems, Information Sciences, 187(3) (2008), 858-870. [15] E. Kilic, K. LeblebicioLu, From classic observability to a simple fuzzy observability for fuzzy discrete-event systems, Information Sciences, 187 (2012), 224-232. [16] R. Kumar, S. Takai,Decentralized Prognosis of Failures in Discrete Event Systems, IEEE Transactions on Automatic Control, 55(1) (2010), 48-59. [17] D. Lefebvre, Fault diagnosis and prognosis with partially observed petri nets, IEEE Transactions on Systems. Man and Cybernetics: Systems, 44(10) (2014), 1413-1424. [18] D. Lefebvre, Probability of current state and future faults with partially observed stochastic petri nets, European Control Conference (ECC), (2014), 258-263. [19] F. Liu, Z. Dziong, Decentralized Diagnosis of Fuzzy Discrete-Event Systems, European Journal of Control, 3 (2012), 304-315. [20] F. C. Liu, Z. Dziong, Reliable decentralized control of fuzzy discrete event systems and a test algorithm, IEEE Trans Cybern, 43 (2013), 321-331. [21] F. Lin, H. Ying, X. Luan, R. D. MacArthur, J. A. Cohn, D. C. BarthJones, L. R. Crane, Fuzzy discrete event systems and its applications to clinical treatment planning, Proco of IEEE Conference on Decision and Control (CDC), 1 (2004), 197-202. [22] F. Lin, H. Ying, R. D. MacArthur, J. A. Cohn, D. Barth-Jones, L. R. Crane, Decision making in fuzzy discrete event systems, Information Science, 177(18) (2007), 3749-3763. [23] F. Lin, Ying H, Modeling and control of fuzzy discrete event systems, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 32 (2002), 408-415. [24] F. Lin, H. Ying, Fuzzy discrete event systems and their observability, Proc of Joint Int Conf of Fuzzy Systems Assoc and World Congr on Fuzzy Inform, 3 (2001), 1271-1276. [25] F. Lui, D. W. Qiu, Diagnosability of fuzzy discrete-event systems: a fuzzy approach, IEEE Transactions on Fuzzy Systems, 17(2) (2009), 372-384. [26] M. Luo , Y. Li, F. Sun, H. Liu, A new algorithm for testing diagnosability of fuzzy discrete event systems, Information sciences, 185 (2012), 100-113. [27] L. D. Mei , Z. Hui , L. W. Chun, S. S. Huang. A fuzzy discrete event system approach for air conditioning system control, Proc. of International Conference on Computer Science and Information Technology, (2008), 791-795. [28] F. Nouioua, P. Dague, L. Ye, Probabilistic analysis of predictability in discrete event systems, Workshop on Principals of Diagnosis, (2014), 185-194. [29] S. Parsons, Current approaches to handling imperfect information in data and knowledge bases, IEEE Transactions on Knowledge and Data Engineering, 8(3) (1996), 353-372. [30] Y. Pencole, Diagnosability analysis of distributed discrete event systems, European Conference on Artificial Intelligence, (2004), 43-47. [31] D. Qiu, Supervisory control of fuzzy discrete event systems: A formal approach, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 35(1) (2005), 72-88. [32] D. Qiu, Fuzzy discrete-event systems under fuzzy observability and a test algorithm, Journal IEEE Transactions on Fuzzy Systems, 17(3) (2009), 578-589. [33] K. Rudie, J. C. Willems, The computational complexity of decentralized discrete-event control problems, IEEE Transactions on Automatic Control, 40(7) (1995), 1313-1319. [34] M. Sayed-Mouchaweh ,Discrete Event Systems Diagnosis and Diagnosability, SpringerBriefs in Electrical and Computer Engineering:Springer-Verlag New York, 2014. [35] M. Traore, E. Chatelet, E. Soulier, H. Gaber, Diagnosis Using Incomplete Model in Fuzzy Discrete Event System: Application to Crisis Management, International Conference on Machine Learning and Applications, (2014), 432- 440. [36] M. Traore, E. Chatelet, E. Soulier, H. Gaber, Fault diagnosis in fuzzy discrete event system: Incomplete models and learning, International Journal of Prognostics and Health Management, 6(2) (2015), 1-12. [37] S. Takai, R. Kumar, Inference-based decentralized prognosis in discrete event systems, IEEE Transactions on Automatic Control, 56(1) (2011), 165-171. [38] S. Takai, Robust prognosability for a set of partially observed discrete event systems, Automatica, 51 (2015), 123-130. [39] L. Ye, P. Dague, F. Nouioua, Predictability analysis of distributed discrete event systems, Proc. Soft Methods for Data Science. Springer, 456 (2013), 5009-5015. [40] H. Ying, F. Lin, R. D. MacArthur, J. A. Cohn, D. C. Barth-Jones, H. Ye, L. R. Crane, A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens, IEEE Transactions on Information Technology in Biomedicine, 10(4) (2006), 663-676. [41] H. Ying, F. Lin, R. D. MacArthur, J. A. Cohn, D. C. Barth-Jones, H. Ye, L. R. Crane, A self-learning fuzzy discrete event system for HIV/AIDS treatment regimen selection, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 37(4) (2007), 966-979. [42] X. Yin, Z. Li, Decentralized fault prognosis of discrete event systems with guaranteed performance bound, Automatica, 69 (2016), 375-379. [43] X. Yin, S. Lafortune, Codiagnosability and coobservability under dynamic observations: Transformation and veri- cation, Automatica, 61 (2015), 241-252. [44] L. A. Zadeh, Fuzzy logic computing with words, IEEE Transactions on Fuzzy Systems, 4(2) (1996), 103-111. | ||
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