Model-Based Monitoring and Diagnosis Chip for Embedded Systems

  • Satoshi Hiratsuka
  • Hsin-Hung Lu
  • Akira Fusaoka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4183)


In this paper, we propose a design consideration for a monitoring and diagnosing chip for the embedded system based on the model-based diagnosis. We introduce the qualitative model for the embedded system by transforming the continuous dynamics of components into the discrete state transition system, which is then further transformed into the circuit called Synchronous Boolean Network(SBN). The faults of system components are reduced to the stuck-at faults in SBN. We present a hardwired SBN diagnosis engine based on Roth’s D-calculus, which allows efficient identification of the faulty parts by propagating the anomaly through the SBN structure.


Embed System Qualitative Model Continuous Dynamic Permanent Fault NAND Gate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balluchi, A., Di Natale, F., Sangiovanni-Vincentelli, A.L., van Schuppen, J.H.: Synthesis for Idle Speed Control of an Automotive Engine. In: Alur, R., Pappas, G.J. (eds.) HSCC 2004. LNCS, vol. 2993, pp. 80–94. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Console, L., Picardi, C., Dupre, D.T.: Temporal Decision Tree: Model-based Diagnosis of Dynamic System On-Board. Journal of Artificial Intelligence Research 19, 469–512 (2003)MATHGoogle Scholar
  3. 3.
    Cascio, F., Console, L., Guagliumi, M., Osella, M., Panati, A., Sottano, S., Dupre, D.T.: Generating on-board diagnosis of dynamic automotive systems based on qualitative models. AI Communications 12(1), 33–43 (1999)Google Scholar
  4. 4.
    Darwiche, A.: Model-based diagnosis using structured system descriptions. Journal of Artificial Intelligence Research 8, 165–222 (1998)MATHMathSciNetGoogle Scholar
  5. 5.
    Dressler, O.: On-Line Diagnosis and Monitoring of Dynamic Systems based on Qualitative Models and Dependency-recording Diagnosis Engines. In: Proc. of 12th European Conference on Artificial Intelligence, pp. 461–465 (1996)Google Scholar
  6. 6.
    Hiratsuka, S., Fusaoka, A.: On a Model-Based Diagnosis for Synchronous Boolean Network. In: Logananthara, R., Palm, G., Ali, M. (eds.) IEA/AIE 2000. LNCS (LNAI), vol. 1821, pp. 198–204. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Roth, J.P.: Diagnosis of Automata Failures: A Calculus and a Methods. IBM Journal of Research and Development 10, 278–291 (1966)MATHCrossRefGoogle Scholar
  8. 8.
    Sachenbacher, M., Struss, P., Calen, C.M.: A prototype for model-based on-board diagnosis of automotive systems. AI Communications 13(2), 83–97 (2000); IEEE trans. on Automatic Control 40(9), 1555–1575Google Scholar
  9. 9.
    Struss, P.: Fundamentals of Model-based Diagnosis of Dynamic Systems. In: Proc. 15th International Conf. on Artificial Intelligence, pp. 480–485 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Satoshi Hiratsuka
    • 1
  • Hsin-Hung Lu
    • 1
  • Akira Fusaoka
    • 1
  1. 1.Department of Human and Computer IntelligenceRitsumeikan UniversityKusatsu-city, SIGAJapan

Personalised recommendations