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A Fuzzy Inference Approach to Fault Diagnosis Refinement in Decentralized Diagnostics

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Advanced Solutions in Diagnostics and Fault Tolerant Control (DPS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 635))

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Abstract

The idea of fuzzy inference approach to fault diagnosis in decentralized, single-level diagnostic structures is introduced in this paper. This approach is particularly intended for large scale industrial systems. The novel and practicable on-line fuzzy fault isolation approach in single-level structure is proposed and discussed. The fuzzy approach allows among others the refinement of the diagnoses particularly when taking into account the uncertainty of the fault symptoms. The proposed approach is depicted in an example. The conclusions regarding expected benefits of the decentralized two-level diagnostic structures summarize the paper.

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References

  1. Alonso-González, C., Rodriguez, J., Prieto, O., Pulido, B.: Ensemble methods and model based diagnosis using possible conflicts and system decomposition. In: Proceedings of the the 23rd International Conference on Industrial Engineering and other Applications of Applied Intelligent Systems, IEA/AIE 2010, pp. 116–125. Springer, Heidelberg (2010)

    Google Scholar 

  2. Bartyś, M.: Generalised reasoning about faults based on diagnostic matrix. Int. J. Appl. Math. Comput. Sci. 23(2), 407–417 (2013)

    MATH  Google Scholar 

  3. Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes - Theory and Application. Prentice-Hall, Englewood Cliffs (1993)

    Google Scholar 

  4. Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M.: Diagnosis and Fault-Tolerant Control. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  5. Boem, F., Ferrari, R.M.G., Parisini, T., Polycarpou, M.: Distributed fault diagnosis for input-output continuous-time nonlinear systems. In: Preprints of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS), Mexico City, Mexico, pp. 1089–1094, 29–31 August 2012

    Google Scholar 

  6. Chen, J., Patton, R.J.: Robust Model Based Fault Diagnosis for Dynamic Systems. Kluver Akademic Publishers, Boston (1999)

    Book  MATH  Google Scholar 

  7. Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001)

    Book  MATH  Google Scholar 

  8. Ding, S., Zhang P.: Observer-based monitoring of distributed networked control systems. In: 6th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Process - Safeprocess 2006, Beijing, P.R. China, Preprints, pp. 337–342 (2006)

    Google Scholar 

  9. Ferrari, R.M.G., Parissini, T., Polycarpou, M.M.: Distributed fault detection and isolation of large-scale discrete-time nonlinear systems: an adaptive approximation approach. IEEE Trans. Autom. Control 57(2), 275–290 (2012)

    Article  MathSciNet  Google Scholar 

  10. Frank, P.M.: Fuzzy supervision. Application of fuzzy logic to process supervision and fault diagnosis. In: International Workshop Fuzzy Technologies in Automation and Intelligent Systems - Fuzzy Duisburg 94, Duisburg, pp. 36–59 (1994)

    Google Scholar 

  11. Frank, P.M., Marcu, T.: Diagnosis strategies and system: principle, fuzzy and neural approaches. In: Teodorescu, H.-N., et al. (eds.) Intelligent Systems and Interfaces. Kulwer, Boston (2000)

    Google Scholar 

  12. Gertler, J.: Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker Inc., New York, Basel, Hong Kong (1998)

    Google Scholar 

  13. Gupta, V., Puig, V., Blesa, J.: A methodology for distributed fault diagnosis. In: 13th European Workshop on Advanced Control and Diagnosis (ACD 2016). IOP Conference Series: Journal of Physics: Conference Series, vol. 783. IOP Publishing (2017)

    Google Scholar 

  14. Himmelblau, D.: Fault Detection and Diagnosis in Chemical and Petrochemical Processes. Elsevier, Amsterdam (1978)

    Google Scholar 

  15. Inagaki, S., Suzuki, T.: Centralized/decentralized fault diagnosis of event-driven systems based on probabilistic inference. In: Guedes, L.A. (ed.) Engineering Control Engineering “Programmable Logic Controller”. InTech, Rijeka (2010)

    Google Scholar 

  16. Indra, S., Travé-Massuyes, L., Chanthery, E.: Decentralized diagnosis with isolation on request for spacecraft. In: Preprints of 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS), Mexico City, Mexico, pp. 283–288, 29–31 August 2012

    Google Scholar 

  17. Isermann, R.: Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance. Springer, New York (2006)

    Google Scholar 

  18. Korbicz, J., Kościelny, J.M., Kowalczuk, Z., Cholewa, W. (eds.): Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer, New York (2004)

    MATH  Google Scholar 

  19. Korbicz, J., Kościelny, J.M. (eds.): Modeling, Diagnostics and Process Control. Implementation in the DiaSter System. Springer, Heildelberg (2010)

    Google Scholar 

  20. Kościelny, J.M.: Diagnostics of processes in decentralised structures. Arch. Control Sci. 7(3–4), 181–202 (1998)

    Google Scholar 

  21. Kościelny, J.M., Sȩdziak, D., Zakroczymski, K.: Fuzzy logic fault isolation in large scale systems. Int. J. Appl. Math. Comput. Sci. 9(3), 637–652 (1999)

    MATH  Google Scholar 

  22. Kościelny, J.M., Syfert, M.: Fuzzy logic application to diagnostics of industrial processes. In: 5th IFAC Symposium SAFEPROCESS 2003, Washington D.C., USA, pp. 771–776 (2003)

    Google Scholar 

  23. Kościelny, J.M., Bartyś, M., Rzepiejewski, P., Sá da Costa, J.M.G.: Actuator fault distinguishability study of the DAMADICS benchmark problem. Control Eng. Pract. 14(6), 645–652 (2006). Elsevier, Pergamon

    Article  Google Scholar 

  24. Kościelny, J.M., Bartyś, M., Syfert, M.: The practical problems of the fault isolation in large scale industrial systems. In: Proceedings of 6th Symposium IFAC Safeprocess 2006, Beijing, Peoples Republic of China, pp. 13–24 (2006). Semi–plenary paper

    Google Scholar 

  25. Kościelny, J.M., Bartyś, M., Syfert, M.: Diagnostics of industrial processes in decentralised structures with application of fuzzy logic. In: 17th World Congress of IFAC, 6–11 July 2008

    Google Scholar 

  26. Kościelny, J.M., Syfert, M.: Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes. Bull. Pol. Acad. Sci. Tech. Sci. 62(3), 571–582 (2014)

    Google Scholar 

  27. Mahalik, N.P.: Fieldbus Technology: Industrial Network Standards for Real-Time Distributed Control. Springer, Heidelberg (2003)

    Book  Google Scholar 

  28. Patton, R., Frank, P., Clark, R. (eds.): Issues of Fault Diagnosis for Dynamic Systems. Springer, Heidelberg (2000)

    Google Scholar 

  29. Pulido, B., Zamarreńo, J.M., Merino, A., Bregon, A.: Using structural decomposition methods to design gray-box models for fault diagnosis of complex industrial systems: a beet sugar factory case study. In: Proceedings of European Conference of the Prognostics and Health Management Society (2012)

    Google Scholar 

  30. Provan, G.: Distributed diagnosability properties of discrete event. In: Proceedings of the American Control Conference 2002, (1), pp. 134–139 (2002)

    Google Scholar 

  31. Simani, S., Fantuzzi, C., Patton, R.J.: Model-based Fault Diagnosis in Dynamic Systems using Identification Techniques. Springer, London (2003)

    Book  Google Scholar 

  32. Stumptner, M., Wotawa, F.: Coupling CSP decomposition methods and diagnosis algorithms for tree-structured systems. In: Proceedings of the 18th International Joint Conference of Artificial Intelligence, IJCAI 2003, pp. 388–393 (2003)

    Google Scholar 

  33. Syfert, M.: A modified algorithm of fault isolation in decentralised structures. In: Kowalczyk, Z. (ed.) Diagnostic of Processes and Systems, Control and Computer Science: Information Technology, Control Theory, Fault and System Diagnosis, PWNT, Gdańsk 2009, pp. 229–236 (2009)

    Google Scholar 

  34. Tsybenko, Y.: Decomposition into independent diagnosis subproblems. In: Qualitative Reasoning Workshop 1995, pp. 173–180. University of Amsterdam (1995)

    Google Scholar 

  35. Witczak, M.: Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems. From Analytical to Soft Computing Approaches. Springer, Berlin (2007)

    MATH  Google Scholar 

  36. Wnuk, P., Kościelny, J.M.: Diagnostic system decomposition with genetic optimization. Pomiary Automatyka Kontrola 6(57), 641–647 (2011)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Michał Syfert, Jan Maciej Kościelny & Michał Bartyś

Authors
  1. Michał Syfert
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  2. Jan Maciej Kościelny
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  3. Michał Bartyś
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Corresponding author

Correspondence to Michał Bartyś .

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Editors and Affiliations

  1. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Jan M. Kościelny

  2. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Michał Syfert

  3. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Anna Sztyber

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Syfert, M., Kościelny, J.M., Bartyś, M. (2018). A Fuzzy Inference Approach to Fault Diagnosis Refinement in Decentralized Diagnostics. In: Kościelny, J., Syfert, M., Sztyber, A. (eds) Advanced Solutions in Diagnostics and Fault Tolerant Control. DPS 2017. Advances in Intelligent Systems and Computing, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-319-64474-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-64474-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64473-8

  • Online ISBN: 978-3-319-64474-5

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