Decentralized prognosis of fuzzy discrete-event systems

Document Type : Research Paper

Authors

1 Computer Science Department, University of Ferhat Abbas Setif 1, Pole 2 - El Bez, 19000 Setif, Algeria

2 Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296, Marseille, France

3 Computer Science Department, Universite de Technologie de Belfort Montbeliard, Rue Thierry Mieg, 90010 Belfort, France

4 Computer Science Department, IUT, University of Haute Alsace, Colmar, France

Abstract

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.

Keywords

References

[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.
 
Iranian Journal of Fuzzy Systems
Volume 16, Issue 3
May and June 2019
Pages 127-143

Files

Share

How to cite

Statistics