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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kościelny, J.M., Syfert, M.: Fuzzy logic application to diagnostics of industrial processes. IFAC Proc. Vol. 36(5), 711–716 (2003)
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)
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)
Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Prentice Hall, New Jersey (1995)
Hailperin, T.: Probability logic. Notre Dame J. Form. Log. 25(3), 198–212 (1984)
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)
Bělohlávek, R., Dauben, J.W., Klir, G.J.: Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press, Oxford (2017)
Zaytoon, J., Lafortune, S.: Overview of fault diagnosis methods for discrete event systems. Annu. Rev. Control 37(2), 308–320 (2013)
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)
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)
Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)
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)
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)
Małgorzata, S.T., Adarshpal, S.S.: A survey of fault localization techniques in computer networks. Sci. Comput. Program. 53(2), 165–194 (2004)
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)
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 2002
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 2002
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kouah, S., Kitouni, I. (2019). Towards Fuzzy Partial Global Fault Diagnosis. In: Dolinina, O., Brovko, A., Pechenkin, V., Lvov, A., Zhmud, V., Kreinovich, V. (eds) Recent Research in Control Engineering and Decision Making. ICIT 2019. Studies in Systems, Decision and Control, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-12072-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-12072-6_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12071-9
Online ISBN: 978-3-030-12072-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)