Skip to main content

Constraint-Driven Fault Diagnosis

  • Chapter
  • First Online:
Fault Diagnosis of Dynamic Systems

Abstract

Constraint-Driven Fault Diagnosis (CDD)  is based on the concept of constraint suspension   [6], which was proposed as an approach to fault detection and diagnosis. In this chapter, its capabilities are demonstrated by describing how it might be applied to hardware systems. With this idea, a model-based fault diagnosis problem may be considered as a Constraint Satisfaction Problem (CSP)  in order to detect any unexpected behavior and Constraint Satisfaction Optimization Problem (COP)  in order to identify the reason for any unexpected behavior because the parsimony principle is taken into account.

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

Access this chapter

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

Institutional subscriptions

References

  1. Bakker, R.R., Dikker, F., Tempelman, F., Wogmim, P.M.: Diagnosing and solving over-determined constraint satisfaction problems. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, IJCAI’93, vol. 1, pp. 276–281. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1993)

    Google Scholar 

  2. Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based constraint satisfaction and optimization. J. ACM 44(2), 201–236 (1997)

    Article  MathSciNet  Google Scholar 

  3. Bose, N.K.: Gröbner bases: an algorithmic method in polynomial ideal theory. In: Multidimensional Systems Theory and Applications, pp. 89–127. Springer Netherlands, Dordrecht (1995)

    Chapter  Google Scholar 

  4. Ceballos, R., Gómez-López, M.T., Gasca, R.M., Del Valle, C.: Determination of possible minimal conflict sets using components clusters and grobner bases. In: Proceedings of the 15th International Workshop on Principles of Diagnosis, DX04, Carcassonne, France, pp. 21–26 (2004)

    Google Scholar 

  5. Cordier, M., Dague, P., Dumas, M., Lévy, F., Montmain, J., Staroswiecki, M., Travé-Massuyès, L.: A comparative analysis of AI and control theory approaches to model-based diagnosis. In: ECAI 2000, Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, Germany, pp. 136–140, 20–25 Aug 2000

    Google Scholar 

  6. Davis, R.: Diagnostic reasoning based on structure and behavior. Artif. Intell. 24(1), 347–410 (1984)

    Article  MathSciNet  Google Scholar 

  7. Dechter, R., Pearl, J.: The anatomy of easy problems: a constraint-satisfaction formulation. In: Proceedings of the 9th International Joint Conference on Artificial Intelligence, Los Angeles, CA, USA, pp. 1066–1072, Aug 1985

    Google Scholar 

  8. Donald, B.R., Kapur, D., Mundy, J.L.: Symbolic and numerical computation for artificial intelligence (1992)

    Google Scholar 

  9. El Fattah, Y., Dechter, R.: Diagnosing tree-decomposable circuits. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI’95, pp. 1742–1749. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995)

    Google Scholar 

  10. Freuder, E., Mackworth, A.: Constraint-Based Reasoning. A Bradford Book. MIT Press (1994)

    Google Scholar 

  11. Freuder, E., Wallace, R.: Partial constraint satisfaction. Artif. Intell. 58, 21–70 (1992)

    Article  MathSciNet  Google Scholar 

  12. Freuder, E.C.: Synthesizing constraint expressions. Commun. ACM 21(11), 958–966 (1978)

    Article  MathSciNet  Google Scholar 

  13. Gasca, R.M., Del Valle, C., Gómez-López, M.T., Ceballos, R.: NMUS: structural analysis for improving the derivation of all MUSes in overconstrained numeric CSPs. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds.) Current Topics in Artificial Intelligence, pp. 160–169. Springer, Berlin, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Gasca, R.M., Ortega, J., Toro, M.: Diagnosis dirigida por restricciones simbólicas para modelos polinómicos. In: Diagnosis, Razonamiento Cualitativo y Sistemas Socieconómicos, pp. 71–78. Carlos Alonso y J. A. Ortega (2001)

    Google Scholar 

  15. Gasca, R.M., Ortega, J.A., Toro, M.: Diagnosis basada en modelos polinomicos usando técnicas simbólicas. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 5(14), 68–77 (2001)

    Google Scholar 

  16. Genesereth, M.R.: The use of design descriptions in automated diagnosis. Artif. Intell. 24(1–3), 411–436 (1984)

    Article  Google Scholar 

  17. Gómez-López, M.T., Ceballos, R., Gasca, R.M., Del Valle, C.: Applying constraint databases in the determination of potential minimal conflicts to polynomial model-based diagnosis. In: Constraint Databases, Proceedings of the 1st International Symposium on Applications of Constraint Databases, CDB’04, Paris, pp. 75–89, 12–13 June 2004

    Google Scholar 

  18. Guernez, C.: Fault detection and isolation on non linear polynomial systems. In: 15th IMACS World Congress on Scientific, Computation, Modelling and Applied Mathematics (1997)

    Google Scholar 

  19. Hoffmann, C.M.: Geometric and Solid Modeling: An Introduction. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1989)

    Google Scholar 

  20. Junker, U.: QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems. In: Proceedings of the 19th National Conference on Artifical Intelligence, AAAI’04, pp. 167–172. AAAI Press (2004)

    Google Scholar 

  21. de Kleer, J.: An assumption-based TMS. Artif. Intell. 28(2), 127–162 (1986)

    Article  Google Scholar 

  22. de Kleer, J., Mackworth, A.K., Reiter, R.: Characterizing diagnoses and systems. Artif. Intell. 56(23), 197–222 (1992)

    Article  MathSciNet  Google Scholar 

  23. Kumar, V.: Algorithms for constraint satisfaction problems: a survey. AI Mag. 13(1), 32–44 (1992)

    MathSciNet  Google Scholar 

  24. Liffiton, M.H., Moffit, M.D., Pollack, M.E., Sakallah, K.A.: Identifying conflicts in overconstrained temporal problems. In: Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI’05, pp. 199–204 (2005)

    Google Scholar 

  25. Liffiton, M.H., Sakallah, K.A.: On finding all minimally unsatisfiable subformulas. In: Proceedings of the 8th International Conference on Theory and Applications of Satisfiability Testing, SAT 2005. Lecture Notes in Computer Science. Springer (2005)

    Google Scholar 

  26. Sachenbacher, M., Williams, B.: Diagnosis as semiring-based constraint optimization. In: Proceedings of the 16th European Conference on Artificial Intelligence, ECAI’04, pp. 873–877. IOS Press, Amsterdam (2004)

    Google Scholar 

  27. Stumptner, M., Wotawa, F.: Diagnosing tree-structured systems. Artif. Intell. 127(1), 1–29 (2001)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work has been partially funded by the Ministry of Science and Technology of Spain (TIN2015-63502-C3-2-R) and the European Regional Development Fund (ERDF/FEDER).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafael Ceballos .

Editor information

Editors and Affiliations

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

Gasca, R.M., Varela-Vaca, Á.J., Ceballos, R. (2019). Constraint-Driven Fault Diagnosis. 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_14

Download citation

Publish with us

Policies and ethics