Modeling process diagnostic knowledge through causal networks

  • Paolo Pogliano
  • Luisella Riccardi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 992)


Diagnosis in process industry is a complex task which can be effectively supported by knowledge based system technology. The majority of applications in this field have focused on rule-based implementations of the heuristic classification approach. In this paper we argue for the exploitation of a causal model based approach to knowledge representation and diagnostic reasoning which can overcome some of the problems manifested by first generation expert systems. The proposed approach can be more viable than other model-based approaches requiring a complete qualitative or quantitative modeling of correct behavior.

The paper presents the causal representation formalism, a definition through a logical framework of the process diagnosis problem considered and the reasoning process which has been defined to solve it. Examples concerning both representation and reasoning issues are taken from DIOGENE, a system devoted to support on-line diagnosis of steam generation process in thermal power plants.


Knowledge Representation Causal Models Diagnostic Reasoning Knowledge-Based Systems 


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  1. [CER 94]
    Cermignani, S. and Tornielli, G.: “Model-based diagnosis of continuous static systems”. Annals of Mathematics and Artificial Intelligence on Model-based Diagnosis, Volume 11, n. 1–4 1994.Google Scholar
  2. [CON 89]
    Console, L., Theseider Dupré, D. and Torasso, P.: “A Theory of Diagnosis for Incomplete Causal Models”. Proc. 11th IJCAI, Detroit (Mi), 1989.Google Scholar
  3. [CRI 92]
    Crippa, G., Pogliano, P., Console, L., Portinale, L., Theseider Dupré, D. and Torasso, P.: “Un Formalismo di Rappresentazione della Conoscenza Causale-Temporale per l'Interpretazione degli Allarmi in un Impianto Termoelettrico”. Technical Report CISE 7256, 1992.Google Scholar
  4. [CRI 93]
    Crippa, G., Pogliano, P., Console, L., Portinale, L., Theseider Dupré, D. and Torasso, P.: “Meccanismi di Ragionamento per la Interpretazione Automatica di Allarmi”. Technical Report CISE 7592, 1993.Google Scholar
  5. [HAK 91]
    Hak-Yeoung Chung, Ik-Soo Park and Sung-Kwang Hur: “Development of an Expert System for Malfunction Diagnosis of Primary System in Nuclear Power Plant”. Proc. Third Symposium on Expert Systems Application to Power Systems, Tokio-Kobe, Japan, 1991.Google Scholar
  6. [NEL 82]
    Nelson, W. R.: “REACTOR: an Expert System for Diagnosis and Treatment of Nuclear Reactor Accidents”. Proc. Second International Conference on Artificial Intelligence, Los Altos, California, 1982.Google Scholar
  7. [NEW 82]
    Newell, A.: “The Knowledge Level”. Artificial Intelligence, Vol. 18, 1982, pp. 87–127.Google Scholar
  8. [PAT 81]
    Patil, R.: “Causal Representation of Patient Illness for Electrolyte and Acid-Based Diagnosis”. MIT/LCS/TR-267, Cambridge, MA, 1981.Google Scholar
  9. [PEN 86]
    Peng, Y. and Reggia, J. A.: “Plausibility of Diagnostic Hypotheses”. Proc. of National Conference on Artificial Intelligence, AAAI, 1986.Google Scholar
  10. [REG 83]
    Reggia, J. A., Nau, D. S. and Wang, P. J.: “Diagnostic expert systems based on a set covering model”. International Journal on Man-Machine Studies, Vol. 19, 1981, pp. 437–460.Google Scholar
  11. [SAC 86]
    Sachs, P. A., Paterson, A. M. and Turner, M. H. M.: “ESCORT — An Expert System for Complex Operations in Real Time”. Expert Systems, Vol. 3, No. 1, Jan 1986, pp. 22–29.Google Scholar
  12. [STE 85]
    Steels, L. and Van de Velde, W.: “Learning in Second Generation Expert Systems”. In Knowledge-based Problem Solving, Kowalik ed., Prentice Hall, 1985.Google Scholar
  13. [TOR 89]
    Torasso, P. and Console, L.: “Diagnostic Problem Solving: Combining Heuristic Approximate and Causal Reasoning”. Van Nostrand Reinhold, 1989.Google Scholar
  14. [TOU 94]
    Tourtier, P. A. and Boyera, S.: “Validating at Early Stages With a Causal Simulation Tool”. Proc. European Knowledge Acquisition Workshop, EKAW'94, Berlin, Germany, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Paolo Pogliano
    • 1
  • Luisella Riccardi
    • 1
  1. 1.CISE-Tecnologie InnovativeMilanItaly

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