Advertisement

Hybrid Bond-Graph Possible Conflicts for Hybrid Systems Fault Diagnosis

  • Carlos J. Alonso-González
  • Belarmino Pulido
  • Anibal Bregon
Chapter

Abstract

Nowadays hybrid systems are everywhere: vehicles, planes, electronic devices, industrial factories, and so on. All these systems exhibit different behavior patterns depending on the actual operation mode. In this work we propose a framework for fault diagnosis of those dynamic systems characterized by continuous behavior commanded by discrete actuators such as valves, bypasses, relays, etc.

One main difficulty in reasoning about hybrid systems is state tracking because we need to distinguish between healthy and faulty states during mode changes. Moreover, there are two kinds of faults in a hybrid system: discrete and parametric. Discrete faults are related to faults in actuators and usually introduce great discontinuities in system dynamics. Parametric faults are related to tear and wear and their effects exhibit slower dynamics.

Our proposal is to use Hybrid Bond-Graphs to extend the model-based diagnosis technique based on the Possible Conflict concept for hybrid systems. Main advantage of the approach is that the complete enumeration of the system operation modes is not necessary.

In this work we will completely characterize Hybrid Bond-Graph Possible Conflicts and provide a unified diagnosis framework for discrete and parametric faults based on tracking the behavior of several subsystems determined by Hybrid Bond-Graphs Possible Conflicts. We will show the technique effectiveness to diagnose both kinds of faults in a complex simulation system made up of four interconnected tanks.

Notes

Acknowledgements

This work has been supported by Spanish MINECO under DPI2013-45414-R grant. The authors would like to thank Noemi Moya Alonso for her contribution on the preliminary stages of this work, particularly the early HPCs characterization, and Alberto Hernández for the implementation of the SHBG-PCs algorithms. First author wants to thank E. Martinez for the colors and the drive.

References

  1. 1.
    Daigle, M. J. (2008, May). A qualitative event-based approach to fault diagnosis of hybrid systems. Ph.D. thesis, Graduate School of Vandebilt University, Nashville, TN.Google Scholar
  2. 2.
    Cocquempot, V., El Mezyani, T., & Staroswiecki, M. (2004). Fault detection and isolation for hybrid systems using structured parity residuals. In 5th Asian Control Conference, July 2004 (Vol. 2, pp. 1204–1212).Google Scholar
  3. 3.
    Lunze, J. (2000). Diagnosis of quantised systems by means of timed discrete-event representations. In Proceedings of the 3rd International Workshop on Hybrid Systems: Computation and Control, HSCC ’00, London, UK (pp. 258–271). Berlin: Springer.Google Scholar
  4. 4.
    Mosterman, P., & Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions. IEEE Transactions on Systems, Man, and Cybernetics—Part A, 29(6), 554–565.Google Scholar
  5. 5.
    Narasimhan, S., & Brownston, L. (2007). Hyde - A general framework for stochastic and hybrid model-based diagnosis. In Proceedings of the 18th International Workshop on Principles of Diagnosis, DX07, Nashville, TN, May 29–31, 2007 (pp. 186–193).Google Scholar
  6. 6.
    Hofbaur, M. W., & Williams, B. C. (2004). Hybrid estimation of complex systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 34(5), 2178 –2191.Google Scholar
  7. 7.
    Benazera, E., & Travé-Massuyès, L. (2009). Set-theoretic estimation of hybrid system configurations. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 39, 1277–1291.Google Scholar
  8. 8.
    Bayoudh, M., Travé-Massuyès, L., & Olive, X. (2008). Towards active diagnosis of hybrid systems. In Proceedings of the 19th International Workshop on Principles of Diagnosis, DX08, Sept 2008, Blue Mountains, Australia.Google Scholar
  9. 9.
    Rienmuller, T., Hofbaur, M., Travé-Massuyès, L., & Bayoudh, M. (2013). Mode set focused hybrid estimation. International Journal of Applied Mathematics and Computer Science, 23(1), 131.Google Scholar
  10. 10.
    Mosterman, P. J., & Biswas, G. (1994). Behavior generation using model switching - A hybrid bond graph modeling technique. In Society for Computer Simulation (pp. 177–182). New York: SCS Publishing.Google Scholar
  11. 11.
    Ould Bouamama, B., Biswas, G., Loureiro, R., & Merzouki, R. (2014). Graphical methods for diagnosis of dynamic systems: Review. Annual Reviews in Control, 38(2), 199–219.Google Scholar
  12. 12.
    Broenink, J. F. (1999). Introduction to physical systems modelling with Bond Graphs. In SiE whitebook on simulation methodologies. University of Twente, Enschede, Netherlands, 1999. Comment Available online at: http://www.ce.utwente.nl/bnk/papers/BondGraphsV2.pdf.
  13. 13.
    Karnopp, D. C., Margolis, D. L., & Rosenberg, R. C. (2006). System Dynamics: Modeling and Simulation of Mechatronic Systems. New York: John Wiley & Sons, Inc.Google Scholar
  14. 14.
    Pulido, B., & Alonso-González, C. (2004). Possible conflicts: A compilation technique for consistency-based diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 34(5), 2192–2206.Google Scholar
  15. 15.
    Bregon, A., Biswas, G., Pulido, B., Alonso-González, C., & Khorasgani, H. (2014). A common framework for compilation techniques applied to diagnosis of linear dynamic systems. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 44(7), 863–876.Google Scholar
  16. 16.
    Bregon, A., Alonso-Gonzalez, C., Biswas, G., Pulido, B., & Moya, N. (2012). Fault diagnosis in hybrid systems using possible conflicts. In Proceedings of the IFAC SAFEPROCESS’12, Mexico D.F., Mexico.Google Scholar
  17. 17.
    Roychoudhury, I., Daigle, M. J., Biswas, G., & Koutsoukos, X. (2010). Efficient simulation of hybrid systems: A hybrid bond graph approach. SIMULATION: Transactions of the Society for Modeling and Simulation International, 87, 467–498.Google Scholar
  18. 18.
    Narasimhan, S., & Biswas, G. (2007, May). Model-based diagnosis of hybrid systems. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 37(3), 348–361.Google Scholar
  19. 19.
    Samantaray, A. K., & Ould Bouamama, B. (2008). Model-based process supervision: A bond graph approach. London: Springer.Google Scholar
  20. 20.
    Moya, N. (2013). Fault Diagnosis of Hybrid Systems with Dynamic Bayesian Networks and Hybrid Possible Conflicts. PhD thesis, ETSI. Informatica. Universidad de Valladolid.Google Scholar
  21. 21.
    Pulido, B., Alonso-González, C., Bregon, A., & Hernández, A. (2015). Characterizing and computing HBG-PCs for hybrid systems fault diagnosis. In Conference of the Spanish Association for Artificial Intelligence (pp. 116–127). Berlin: Springer.Google Scholar
  22. 22.
    Bregon, A., Biswas, G., & Pulido, B. (2012). A decomposition method for nonlinear parameter estimation in TRANSCEND. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 42(3), 751–763.Google Scholar
  23. 23.
    Bregon, A., Alonso, C., & Pulido, B. (2015). Improving fault isolation and identification for hybrid systems with hybrid possible conflicts. In Proceedings of the XXVI International Workshop on Principles of Diagnosis, DX’15, Paris, France (pp. 59–66).Google Scholar
  24. 24.
    Prakash, O., & Samantaray, A. K. (2017). Model-based diagnosis and prognosis of hybrid dynamical systems with dynamically updated parameters. In Bond Graphs for Modelling, Control and Fault Diagnosis of Engineering Systems (pp. 195–232), Switzerland: Springer.Google Scholar
  25. 25.
    Prakash, O., Samantaray, A. K., Bhattacharyya, R. (2017). Model-based diagnosis of multiple faults in hybrid dynamical systems with dynamically updated parameters. IEEE Transactions on Systems, Man, and Cybernetics: Systems, PP, 1–20.Google Scholar
  26. 26.
    Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555–1575.MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Bregon, A., Alonso-González, C. J., & Pulido, B. (2014). Integration of simulation and state observers for online fault detection of nonlinear continuous systems. IEEE Transactions on Systems Man and Cybernetics: Systems, 44(12), 1553–1568.CrossRefGoogle Scholar
  28. 28.
    Daigle, M., Bregon, A., & Roychoudhury, I. (2015). A structural model decomposition framework for hybrid systems diagnosis. In Proceedings of the 26th International Workshop on Principles of Diagnosis, DX’15, Sept 2015, Paris, France.Google Scholar
  29. 29.
    Bregon, A., Daigle, M., & Roychoudhury, I. (2016). Qualitative fault isolation of hybrid systems: A structural model decomposition-based approach. In Third European Conference of the PHM Society, July 2016.Google Scholar
  30. 30.
    Feng, W., Qin, R., Zhang, W., & Zhao, Q. (2016). A possible conflicts based distributed diagnosis method for hybrid system. In 2016 Prognostics and System Health Management Conference (PHM-Chengdu), Oct 2016 (pp. 1–6).Google Scholar
  31. 31.
    Bregon, A., Daigle, M., Roychoudhury, I., Biswas, G., Koutsoukos, X., & Pulido, B. (2014). An event-based distributed diagnosis framework using structural model decomposition. Artificial Intelligence, 210, 1–35.CrossRefzbMATHGoogle Scholar
  32. 32.
    Sayed-Mouchaweh, M., & Lughofer, E. (2015). Decentralized fault diagnosis approach without a global model for fault diagnosis of discrete event systems. International Journal of Control, 88(11), 2228–2241.MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., & Schröder, J. (2006). Diagnosis and fault-tolerant control (Vol. 691). Berlin: Springer.zbMATHGoogle Scholar
  34. 34.
    Travé-Massuyes, L., Escobet, T., & Olive, X. (2006). Diagnosability analysis based on component-supported analytical redundancy relations. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 36(6), 1146–1160.CrossRefGoogle Scholar
  35. 35.
    Sayed-Mouchaweh, M. (2014). Discrete event systems: Diagnosis and diagnosability. Berlin: Springer Science & Business Media.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Carlos J. Alonso-González
    • 1
  • Belarmino Pulido
    • 1
  • Anibal Bregon
    • 1
  1. 1.Departamento de InformáticaUniversidad de ValladolidValladolidSpain

Personalised recommendations