Skip to main content
SpringerLink
Log in
Book cover

Fault Diagnosis of Dynamic Systems pp 125–152Cite as

Model-Based Diagnosis by the Artificial Intelligence Community: Alternatives to GDE and Diagnosis of Dynamic Systems

  • Chapter
  • First Online:

Abstract

In this chapter, we analyze main problems found by the Artificial Intelligence approach to Model-based diagnosis (DX): the online computation of minimal conflicts by means of an ATMS-like dependency-recording engine, and the need for an extension to deal with dynamic systems diagnosis. To cope with the first problem we will see different options: from extensions to the original GDE to the description of several topological methods, explaining deeply one of them: the Possible Conflict (PC) approach, and its relation with minimal conflicts and ARRs. To cope with the second problem, dynamics, we review the whole set of proposals made to extend Reiter’s formalization and the GDE to dynamic systems: from GDE extensions to the natural extension of topological methods to include temporal information. In this chapter we provide the complete extension of the PCs approach to diagnose dynamic systems, and their relation not only with ARRs, but with another FDI proposals for systems tracking: state-observers.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Software for PCs computation is available at: http://www.infor.uva.es/~belar/SoftwareCPCs/.

  2. 2.

    In this context, by causality assignment we mean every possible way one variable in one equation can be solved assuming the remaining variables are known.

  3. 3.

    In FDI terminology, a conflict arises when the residual deviates significantly from zero.

  4. 4.

    Equations \(\{e_{10}, e_{11}, e_{12}\}\) define the observational model, just linking each internal variable in \(\mathcal {X}\) with its sensor in \(\mathcal {U}=\{q_i\}\) or \(\mathcal {Y}=\{h_{T1,obs}, h_{T3,obs}, q_{23,obs}\}\).

  5. 5.

    In the structural approach defined by Staroswiecki, the structural model defines a bipartite graph for the constraints and the unknown variables in the system. The matching in the definition refers to a matching in that bipartite graph. The reader can find additional information in those structural issues in the work by Blanke et al. [3] and in Chap. 3 in this book.

  6. 6.

    This is easy to understand because MECs are built adding one constraint in each step to determine an unknown variable, mimicking how the CBD computational paradigm, GDE, works online [26, 36].

  7. 7.

    Following the convention in [26], fault candidates are presented in brackets.

References

  1. Armengol, J., Bregon, A., Escobet, T., Gelso, E., Krysander, M., Nyberg, M., Olive, X., Pulido, B., Travé-Massuyès, L.: Minimal structurally overdetermined sets for residual generation: a comparison of alternative approaches. In: Proceedings of the IFAC-Safeprocess 2009. Barcelona, Spain (2009)

    Article  Google Scholar 

  2. Biswas, G., Simon, G., Mahadevan, N., Nararsimhan, S., Ramirez, J., Karsai, G.: A robust method for hybrid diagnosis of complex systems. In: Proceeding of the 5th IFAC Symposium on Fault Detection. Supervision and Safety of Technical Processes, SAFEPROCESS03, pp. 1125–1130. Washington D.C, USA (2003)

    Article  Google Scholar 

  3. Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M.: Diagnosis and Fault-Tolerant Control. Springer, Berlin (2006)

    MATH  Google Scholar 

  4. Bousson, K., Travé-Massuyès, L., Zimmer, L.: Causal model-based diagnosis of dynamic systems. LAAS Report No 94231 (1994)

    Google Scholar 

  5. Bregon, A., Alonso-González, C.J., Pulido, B.: Integration of simulation and state observers for online fault detection of nonlinear continuous systems. IEEE Trans. Syst. Man Cybern.: Syst. 44(12), 1553–1568 (2014)

    Article  Google Scholar 

  6. Bregon, A., Biswas, G., Pulido, B.: A decomposition method for nonlinear parameter estimation in TRANSCEND. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Humans 42(3), 751–763 (2012)

    Article  Google Scholar 

  7. Bregon, A., Biswas, G., Pulido, B., Alonso-González, C., Khorasgani, H.: A common framework for compilation techniques applied to diagnosis of linear dynamic systems. IEEE Trans. Syst. Man. Cybern.: Syst. 44(7), 863–876 (2014). https://doi.org/10.1109/TSMC.2013.2284577

    Article  Google Scholar 

  8. Broenink, J.: Introduction to Physical Systems Modelling with Bond Graphs. SiE Whitebook on Simulation Methodologies (1999)

    Google Scholar 

  9. Brusoni, V., Console, L., Terenziani, P., Dupré, D.T.: A spectrum of definitions for temporal model-based diagnosis. Artif. Intell. 102(1), 39–79 (1998)

    Article  MathSciNet  Google Scholar 

  10. Chantler, M., Daus S. Vikatos, T., Coghill, G.: The use of quantitative dynamic models and dependency recording engines. In: Proceedings of the Seventh International Workshop on Principles of Diagnosis (DX-96), pp. 59–68. Val Morin, Quebec, Canada (1996)

    Google Scholar 

  11. Chittaro, L., Guida, G., Tasso, C., Toppano, E.: Functional and teleological knowledge in the multimodeling approach for reasoning about physical systems: a case study in diagnosis. IEEE Trans. Syst. Man Cybern. 23(6), 1718–1751 (1993)

    Article  Google Scholar 

  12. Chittaro, L., Ranon, R.: Hierarchical diagnosis guided by observations. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence-Volume 1, pp. 573–578. Morgan Kaufmann Publishers Inc., Burlington (2001)

    Google Scholar 

  13. Cordier, M.O., Dague, P., Lévy, F., Montmain, J., Staroswiecki, M., Travé-Massuyès, L.: Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the Artificial Intelligence and Automatic Control perspectives. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(5), 2163–2177 (2004)

    Article  Google Scholar 

  14. Dague, P.: Model-based diagnosis of analog electronic circuits. Ann. Math. Artif. Intell. 11(1), 439–492 (1994). https://doi.org/10.1007/BF01530755

  15. Dague, P., Deves, P., Luciani, P., Taillibert, P.: Analog systems diagnosis. In: Proceedings of European Conference on Artificial Intelligence, ECAI, pp. 173–178 (1990)

    Google Scholar 

  16. Dressler, O.: On-line diagnosis and monitoring of dynamic systems based on qualitative models and dependency-recording diagnosis engines. In: Proceedings of the Twelfth European Conference on Artificial Intelligence (ECAI-96), pp. 461–465 (1996)

    Google Scholar 

  17. Dressler, O., Freitag, H.: Prediction sharing across time and contexts. In: Proceedings of the Eighth International Workshop on Qualitative Reasoning about Physical Systems (QR-94), pp. 63–68. Nara, Japan (1994)

    Google Scholar 

  18. Forbus, K.: Qualitative reasoning about physical processes. In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81). Vancouver, Canada (1981)

    Google Scholar 

  19. Frisk, E., Dustegor, D., Krysander, M., Cocquempot, V.: Improving fault isolability properties by structural analysis of faulty behavior models: application to the DAMADICS benchmark problem. In: Proceedings of SAFEPROCESS-2003. Washington, DC, USA (2003)

    Google Scholar 

  20. Guckenbiehl, T., Schäfer-Richter, G.: SIDIA: extending prediction based diagnosis to dynamic models. In: Expert Systems in Engineering Principles and Applications, pp. 53–68. Springer (1990)

    Google Scholar 

  21. Hamscher, W., Davis, R.: Diagnosing circuits with state: An inherently underconstrained problem. In: Proceedings of AAAI, pp. 142–147 (1984)

    Google Scholar 

  22. Karnopp, D., Rosenberg, R., Margolis, D.: System Dynamics, A Unified Approach, 3rd edn. Wiley, New York (2000)

    Google Scholar 

  23. de Kleer, J.: An assumption-based TMS. Artif. Intell. 28, 127–162 (1986)

    Article  Google Scholar 

  24. de Kleer, J.: Extending the ATMS. Artif. Intell. 28, 163–196 (1986)

    Article  Google Scholar 

  25. de Kleer, J.: Problem solving with the ATMS. Artif. Intell. 28, 197–224 (1986)

    Article  Google Scholar 

  26. de Kleer, J., Williams, B.: Diagnosing multiple faults. Artif. Intell. 32(1), 97–130 (1987)

    Article  Google Scholar 

  27. Krysander, M., Åslund, J., Nyberg, M.: An efficient algorithm for finding minimal overconstrained subsystems for model-based diagnosis. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Humans 38(1), 197–206 (2008)

    Article  Google Scholar 

  28. Loiez, E., Taillibert, P.: Polynomial temporal band sequences for analog diagnosis. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 474–479. Nagoya, Japan (1997)

    Google Scholar 

  29. Manders, E.J., Narasimhan, S., Biswas, G., Mosterman, P.J.: A combined qualitative/quantitative approach for fault isolation in continuous dynamic systems. SafeProcess, Budapest, Hungary (2000)

    Article  Google Scholar 

  30. Milne, R., Travé-Massuyès, L.: Model based aspects of the TIGER gas turbine condition monitoring system. IFAC Proc. Vol. 30(18), 405–410 (1997)

    Article  Google Scholar 

  31. Mosterman, P., Biswas, G.: Diagnosis of continuous valued systems in transient operating regions. IEEE Trans. Syst. Man Cybern. 29(6), 554–565 (1999)

    Article  Google Scholar 

  32. Narasimhan, S., Biswas, G.: Model-based diagnosis of hybrid systems. IEEE Trans. Syst. Man Cybern. Part A 37(3), 348–361 (2007). https://doi.org/10.1109/TSMCA.2007.893487

    Article  Google Scholar 

  33. Oyeleye, O., Finch, F., Kramer, M.: Qualitative modeling and fault diagnosis of dynamic processes by MIDAS. Chem. Eng. Commun. 96, 205–228 (1990)

    Article  Google Scholar 

  34. Puig, V., Quevedo, J., Escobet, T., Meseguer, J.: Toward a better integration of passive robust interval-based FDI algorithms. In: Proceedings of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS06. Beijing, China (2006)

    Google Scholar 

  35. Pulido, B., Alonso, C., Acebes, F.: Consistency-based diagnosis of dynamic systems using quantitative models and off-line dependency-recording. In: 12th International Workshop on Principles of Diagnosis (DX-01), pp. 175–182. Sansicario, Italy (2001)

    Google Scholar 

  36. Pulido, B., Alonso-González, C.: Possible Conflicts: a compilation technique for Consistency-based diagnosis. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34(5), 2192–2206 (2004)

    Article  Google Scholar 

  37. Pulido, B., Bregon, A., Alonso-González, C.: Analyzing the influence of differential constraints in possible conflicts and ARR computation. In: Meseguer, P., Mandow, L., Gasca, R.M. (eds.) Current Topics in Artficial Intelligence, CAEPIA 2009 Selected Papers. Springer, Berlin (2010)

    Chapter  Google Scholar 

  38. Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)

    Article  MathSciNet  Google Scholar 

  39. Roychoudhury, I., Biswas, G., Koutsoukos, X.: Designing distributed diagnosers for complex continuous systems. IEEE Trans. Autom. Sci. Eng. 6(2), 277–290 (2009)

    Article  Google Scholar 

  40. Staroswiecki, M.: A structural view of fault-tolerant estimation. In: Proceedings of IMechE. Part I: J. Systems and Control Engineering, vol. 221 (2007)

    Google Scholar 

  41. Struss, P.: Fundamentals of model-based diagnosis of dynamic systems. In: Proceedings of the Fifteenth International Joint Conference on Artifical Intelligence (IJCAI-97), pp. 480–485. Nagoya, Japan (1997)

    Google Scholar 

  42. Travé-Massuyès, L.: Model based thoughts Ca-En and TIGER then and now. In: Bundy, A., Wilson, S. (eds.) ROB MILNE: A Tribute to a Pioneering AI Scientist, Entrepreneur and Mountaineer, pp. 1–28. IOS Press (2006). ISBN 1-58603-639-4

    Google Scholar 

  43. Travé-Massuyès, L., Escobet, T., Pons, R., Tornil, S.: The Ca-En diagnosis system and its automatic modelling method. Computación y Sistemas 5(2), 128–143 (2001)

    Google Scholar 

  44. Travé-Massuyès, L., Pons, R.: Causal ordering for multiple mode systems. In: Proceedings of the Eleventh International Workshop on Qualitative Reasoning-QR97, pp. 203–214 (1997)

    Google Scholar 

  45. Williams, B.: Doing time: putting qualitative reasoning on firmer ground. In: Proceedings of the Fifth AAAI National Conference on Artificial Intelligence (AAAI-86), pp. 105–112. Philadelphia, Pennsylvania, USA (1986)

    Google Scholar 

  46. Williams, B.: Temporal qualitative analysis: explaining how physical systems work. In: Readings in Qualitative Reasoning about Physical Systems, pp. 133–177. Morgan-Kaufmann Pub., San Mateo (1990). Revised version of Qualitative analysis of MOS circuits, in Artificial Intelligence, vol. 24, No. 1-3 pp. 281–346 (1984)

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the valuable contributions of Anibal Bregon, Teresa Escobet, Louise Travè-Massuyés, and Renaud Pons for the material related to Ca\(\sim \)En, TRANSCEND, and the BRIDGE references.

Author information

Authors and Affiliations

  1. Departamento de Informática, Universidad de Valladolid, Valladolid, Spain

    Belarmino Pulido & Carlos J. Alonso-González

Authors
  1. Belarmino Pulido
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos J. Alonso-González
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Belarmino Pulido .

Editor information

Editors and Affiliations

  1. Research Center for Supervision, Safety and Automatic Control (CS2AC), Universitat Politècnica de Catalunya (UPC), Terrassa, Spain

    Teresa Escobet

  2. Department of Computer Science, University of Valladolid, Segovia, Spain

    Anibal Bregon

  3. Department of Computer Science, University of Valladolid, Valladolid, Spain

    Belarmino Pulido

  4. Research Center for Supervision, Safety and Automatic Control (CS2AC), Universitat Politècnica de Catalunya (UPC), Terrassa, Spain

    Vicenç Puig

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pulido, B., Alonso-González, C.J. (2019). Model-Based Diagnosis by the Artificial Intelligence Community: Alternatives to GDE and Diagnosis of Dynamic Systems. In: Escobet, T., Bregon, A., Pulido, B., Puig, V. (eds) Fault Diagnosis of Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-17728-7_6

Download citation

Publish with us

Policies and ethics

Search

Navigation