Monitoring of Hybrid Dynamic Systems: Application to Chemical Process

  • Nelly Olivier-Maget
  • Gilles Hetreux


These works present a fault detection and isolation methodology for the monitoring of Hybrid Dynamic Systems. The developed methodology rests on a mixed approach which combines a model-based method for the fault detection and an approach based on data (pattern matching) for the identification of fault(s). It is divided into three parts: The first part concerns the reconstruction of the state of the system, thanks to the extended Kalman filter and the generation of the residuals by comparison between the predicted behaviour (obtained thanks to the simulation of the reference model) and the real observed behaviour (estimated by the extended Kalman filter).The second part exploits these residuals for the generation of a synthetic structure: the non-binary signatures. The last part deals with the diagnosis of the fault and is based on a problem of pattern matching: the signature obtained in the previous part is compared with the theoretical fault signatures by means of distance. Its use is illustrated by the studies of diagnosis problems in the field of Chemical Process System Engineering.


Fault detection and diagnosis Hybrid dynamic systems Generation of non-binary signatures Manhattan distance Extended Kalman filter 


  1. 1.
    Olivier-Maget, N., Hétreux, G., Le Lann, J. M., & Le Lann, M. V. (2009). Model-based fault diagnosis for hybrid systems: Application on chemical processes. Computers & Chemical Engineering, 33(10), 1617–1630.CrossRefGoogle Scholar
  2. 2.
    Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis. Computers & Chemical Engineering, 27, 293–346.CrossRefGoogle Scholar
  3. 3.
    De Kleer, J. (1986). An assumption-based TMS. Artificial Intelligence, 28, 127–162.CrossRefGoogle Scholar
  4. 4.
    Birouche, A. (2006). Contribution sur la synthèse d’observateurs pour les systèmes dynamiques hybrides. Thèse de doctorat, Institut National Polytechnique de Lorraine, Nancy, France.Google Scholar
  5. 5.
    Ding, S. X. (2014). Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results. Journal of Process Control, 24(2), 431–449.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Clark, R. N., Fosth, D. C., & Walton, V. M. (1975). Detection instrument malfunctions in control systems. IEEE Transactions on Aerospace and Electronic Systems, AES-11, 465–473.CrossRefGoogle Scholar
  7. 7.
    Jazwinski, A. H. (1970). Stochastic processes and filtering theory, Mathematics in Science and Engineering (Vol. 64). New York: Academic Press.CrossRefzbMATHGoogle Scholar
  8. 8.
    Reif, K., & Unbehauen, R. (1999). The extended Kalman filter as an exponential observer for nonlinear systems. IEEE Transactions on Signal Processing, 47(8), 2324–2328.MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Olivier-Maget, N., Hétreux, G., Le Lann, J. M., & Le Lann, M. V. (2009). Dynamic state reconciliation and model-based fault detection for chemical processes. Asia Pacific Journal of Chemical Engineering, 4(6), 929–941.CrossRefGoogle Scholar
  10. 10.
    Olivier-Maget, N. (2008). Surveillance des systèmes dynamiques hybrides: Application aux procédés. Thèse de doctorat, Université de Toulouse, France.Google Scholar
  11. 11.
    Sayed-Mouchaweh, M. (2016). Learning from data streams in dynamic environments, Springer briefs in electrical and computer engineering (p. 75). Cham: Springer. ISBN: 978-3-319-25665-8.Google Scholar
  12. 12.
    Chin, H., & Danai, K. (1991). A method of fault signature extraction for improved diagnosis. In IEEE ACC Conference, Boston, USA.Google Scholar
  13. 13.
    Fang, C. Z., & Ge, W. (1998). Failure isolation in linear systems. In IMACS 12th world congress, Paris, France, pp. 442–446.Google Scholar
  14. 14.
    Gertler, J., & Singer, D. (1990). A new structural framework for parity equation based failure detection and isolation. Automatica, 26(2), 381–388.MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Saporta, G. (1990). Probabilités, analyse des données et statistique. Paris: Éditions Technip.zbMATHGoogle Scholar
  16. 16.
    Cassar, J. P., Litwak, R.-G., Cocquempot, V., & Staroswiecki, M. (1994). Approche structurelle de la conception de systèmes de surveillance pour les procédés industriels. Diagnostic et Sûreté de Fonctionnement, 4(2), 179–202.Google Scholar
  17. 17.
    Kaufmann, A. (1977). Introduction à la théorie des sous-ensembles flous à l'usage des ingénieurs. Tomes I et II, Masson.Google Scholar
  18. 18.
    Theillol, D., Weber, P., Ghetie, M., & Noura, H. (1995). A hierarchical fault diagnosis method using a decision support system applied to a chemical plant. In International Conference on Systems, Man, and Cybernetics, Canada.Google Scholar
  19. 19.
    Ripoll, P. (1999). Conception d’un système de diagnostic flou appliqué au moteur automobile. Thèse de doctorat, Université de Savoie, France.Google Scholar
  20. 20.
    Koscielny, J. M. (1993). Method of fault isolation for industrial processes. Diagnostic et Sûreté de Fonctionnement, 3(2), 205–220.Google Scholar
  21. 21.
    Olivier-Maget, N., & Hétreux, G. (2016). Fault detection and isolation for industrial risk prevention. Journal Européen des Systèmes Automatisés, 49(4-5), 537–557.CrossRefGoogle Scholar
  22. 22.
    Joglekar, G. S., & Reklaitis, G. V. (1985). A simulator for batch and semi-continuous processes. Computers and Chemical Engineering, 8(6), 315–327.CrossRefGoogle Scholar
  23. 23.
    Olivier-Maget, N., Hétreux, G., Le Lann, J. M., & Le Lann, M. V. (2008). Integration of a failure monitoring within a hybrid dynamic simulation environment. Chemical Engineering and Processing: Process Intensification, 47(11), 1942–1952.CrossRefGoogle Scholar
  24. 24.
    Toubakh, H., & Sayed-Mouchaweh, M. (2016). Hybrid dynamic classifier for drift-like fault diagnosis in a class of hybrid dynamic systems: Application to wind turbine converters. Neurocomputing, 171, 1496–1516.CrossRefGoogle Scholar
  25. 25.
    Einicke, G. A., & White, L. B. (1999). Robust extended Kalman filtering. IEEE Transactions on Signal Processing, 47(9), 2596–2599.CrossRefzbMATHGoogle Scholar
  26. 26.
    De Kleer, J., & Williams, B. C. (1987). Diagnosing multiple faults. Artificial Intelligence, 32, 97–130.CrossRefzbMATHGoogle Scholar
  27. 27.
    Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591–611.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratoire de Génie Chimique, Université de Toulouse, CNRSToulouseFrance

Personalised recommendations