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Towards Fuzzy Partial Global Fault Diagnosis

  • Sofia KouahEmail author
  • Ilham Kitouni
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)

Abstract

Diagnosis aims at identifying a faulty system based on its behavior observations. It is widely emerged in altogether computer sciences fields, among others: aeronautics, space exploration, nuclear energy, process industries, manufacturing, healthcare, networking, automatism and many other control applications. Diagnosis involves distributed components with an uncertain global view. This paper intends to provide an efficient fuzzy based diagnosis mechanism. Such mechanism enables local hosts’ diagnosis. These local decisions could be merged to provide the global diagnosis. The fuzziness choice is motivated by the fact of incomplete and uncertain system descriptions and observations. Also it is justified by the difficulties of obtaining a complete viewpoint of all system parts where the control is distributed. Our diagnosis mechanism, named FPGD for Fuzzy Partial Global Diagnosis consists of two main steps: Firstly, each remote control host detects and localizes abnormal behaviors which results on a local diagnosis. Each host proceeds by applying a recovery planned actions to maintain system functioning. Furthermore, such local diagnoses should be sent to the global part in order to be merged and analyzed, hence giving a precise and exhaustive global diagnosis. The automatic diagnosis reasoning is a fuzzy system; which based on fuzzy rules, handles incomplete information to deduce system malfunctioning.

Keywords

Complex system Diagnosis Fuzzy logic Internet of things 

References

  1. 1.
    Kościelny, J.M., Syfert, M.: Fuzzy logic application to diagnostics of industrial processes. IFAC Proc. Vol. 36(5), 711–716 (2003)CrossRefGoogle Scholar
  2. 2.
    Katipamula, S., Brambley, M.R.: Methods for fault detection, diagnostics, and prognostics for building systems—a review, part i. HVAC&R Res. 11(1), 3–25 (2005)CrossRefGoogle Scholar
  3. 3.
    Katipamula, S., Brambley, M.R.: Methods for fault detection, diagnostics, and prognostics for building systems—a review, part ii. HVAC&R Res. 11(2), 169–187 (2005)CrossRefGoogle Scholar
  4. 4.
    Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Prentice Hall, New Jersey (1995)zbMATHGoogle Scholar
  5. 5.
    Hailperin, T.: Probability logic. Notre Dame J. Form. Log. 25(3), 198–212 (1984)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Kouah, S., Saidouni, D.E.: Fuzzy labeled transition refinement tree: application to stepwise designing multi agent systems. In: Fuzzy Systems: Concepts, Methodologies, Tools, and Applications, pp. 873–905. IGI global (2017)Google Scholar
  7. 7.
    Bělohlávek, R., Dauben, J.W., Klir, G.J.: Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press, Oxford (2017)zbMATHGoogle Scholar
  8. 8.
    Zaytoon, J., Lafortune, S.: Overview of fault diagnosis methods for discrete event systems. Annu. Rev. Control 37(2), 308–320 (2013)CrossRefGoogle Scholar
  9. 9.
    Grastien, A., Travé-massuyès, L., Puig, C.V.: Solving diagnosability of hybrid systems via abstraction and discrete event techniques. In: 20th world congress of the international federation of automatic control, IFAC 2017, Toulouse, France, 9–14 July 2017, Proceedings Book, pp. 5023–5028 (2017)CrossRefGoogle Scholar
  10. 10.
    Marco, M., Li, Y.: A diagnostic system for gas turbines using GPA-index. In: Proceedings of 18th International Congress COMADEM, pp. 307–322. Cranfield University Press, England (2005)Google Scholar
  11. 11.
    Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Leung, R., Lau, H.C., Kwong, C.K.: An expert system to support the optimization of ion plating process: an OLAP-based fuzzy-cum-ga approach. Expert Syst. Appl. 25(3), 313–330 (2003)CrossRefGoogle Scholar
  13. 13.
    Gautam, K.K., Bhuria, V.: Application of fuzzy logic in power transformer fault diagnosis. In: Proceeding of International Conference on Advanced Computing, Communication and Networks—CCN 2011, pp. 834–839. The IRED Publisher (2011)Google Scholar
  14. 14.
    Małgorzata, S.T., Adarshpal, S.S.: A survey of fault localization techniques in computer networks. Sci. Comput. Program. 53(2), 165–194 (2004)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Deng, R.H., Lazar, A.A., Wang, W.: A probabilistic approach to fault diagnosis in linear lightwave networks. In: Hegering, H.G., Yemini, Y. (eds.) Integrated Network Management III, North-Holland, Amsterdam, pp. 697–708 (1993)CrossRefGoogle Scholar
  16. 16.
    Steinder, M., Sethi, A.S.: End-to-end service failure diagnosis using belief networks. In: Stadler, R., Ulema, M. (eds.) Proceedings of Network Operation and Management Symposium, Florence, Italy, pp. 375–390, April 2002Google Scholar
  17. 17.
    Wietgrefe, H.: Investigation and practical assessment of alarm correlation methods for the use in GSM access networks. In: Stadler, R., Ulema, M. (eds.) Proceedings of Network Operation and Management Symposium, Florence, Italy, pp. 391–404, April 2002Google Scholar
  18. 18.
    Satadru, D., Mohon, S., Pierluigi, P., Beshah, A.: Sensor fault detection, isolation, and estimation in lithium-ion batteries. IEEE Trans. Control Syst. Technol. 24(6), 2141–2149 (2016)CrossRefGoogle Scholar
  19. 19.
    Dey, S., Ayalew, B., Pisu, P.: Nonlinear robust observers for state of charge estimation of lithium-ion cells based on a reduced electrochemical model. IEEE Trans. Control Syst. Technol. 23(5), 1935–1942 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.RELA(CS) LaboratoryUniversity of Larbi Ben M’HidiOum El BouaghiAlgeria
  2. 2.MISC LaboratoryUniversity of Abdelhamid Mehri—Constantine 2Ali MendjeliAlgeria

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