Abstract
Knowledge involved in diagnosis of real complex systems comes from human experts and requires appropriate discrete and qualitative representation. The large amount of information resulted is difficult to be managed and prepared to enter the diagnosis system without the help of an appropriate tool. The paper proposes a knowledge elicitation scheme for multifunctional conductive flow systems under fault diagnosis along with appropriate representation of normative and faulty models. Prototype and instance manifestations get a semi-qualitative representation and symptoms refer to means-end and bond–graph entities in a new approach, suited to human diagnostician’s conceptual view. The Computer Aided Knowledge Elicitation (CAKE) tool proposed copes with knowledge involved in the diagnosis. The case study on knowledge elicitation for fault diagnosis of a hydraulic installation and the conclusions highlight advantages of the present approach.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ariton, V. (2001) Abstraction levels for the fault isolation in multifunctional conductive flow systems. Proceedings of LSS2001 - The 9th IFAC/IFORS/IMACS/IFIP/ Symposium on Large Scale Systems: Theory and Applications, Bucharest, pp. 386–391.
Ariton, V. (2003) Deep and shallow knowledge in fault diagnosis, LNAI 2773, Palade V. Howlett R. J., Lakhmi J. (eds), Springer Verlag, (2003), pp.748–755.
Ariton, V. and Palade, V. (2004) Human-like Fault Diagnosis Using a Neural Network Implementation of Plausibility and Relevance, Neural Computing & Applications, Publisher: Springer-Verlag London Ltd, pp. 1433–3058 (Online).
F. E. Cellier (1995) Modeling from physical principles W. S. Levine (Eds) The Control Handbook CRC Press Boca Raton 98–108
Cherkassky, V. and Lari-Najafi, H. (1992) Data representation for diagnostic neural networks, IEEE Expert, pp. 43–53.
Dubois, D. and Prade, H. (1998) Possibility theory: qualitative and quantitative aspects, in Handbook of Defeasible Reasoning and Uncertainty Management Systems, Smets P. Vol. 1 (ed), Kluwer Acad. Publ., pp. 120–159.
Larsson, J. E.(1992) Knowledge-based methods for control systems, PhD Thesis, Lund.
Mosterman P. J., Biswas G. (2002). A hybrid modeling and simulation methodology for dynamic physical systems. in SIMULATION: Transactions of The Society for Modeling and Simulation International. Vol. 78(1) pp. 5–17.
Mosterman, P. J., Kapadia, R. and Biswas, G. (1995) Using bond graphs for diagnosis of dynamical physical systems, Sixth International Conference on Principles of Diagnosis, pp. 81–85.
R. Kruse J. Gebhardt (1994) Foundations of Fuzzy Systems Willey New York
B. J. Kuipers (1994) Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge MIT Press Cambridge, MA
O’Brien, T. (1970) Reliability of Multifunction Structures, N. Y. University.
Okuda K. and Miyasaka, N.(1991) Model based intelligent monitoring and real time diagnosis, in Preprints of SAFEPROCESS ’91, Isermann, R. (ed), pp. 61–55.
Opdahl, A. L. and Sindre, G.(1994) A taxonomy for real-world modelling concepts, Information Syst., 19(3), Pergamon, pp.229–241.
Shibata, B., Tateno, S., Tsuge, Y. and Matsuyama, H.(1991) Fault diagnosis of the chemical process utilizing signed directed graph, in Preprints of Fault Detection Supervision and Safety for technical Processes - SAFEPROCESS ’91, Isermann, R. (ed), pp. 381–386.
Struss P. (1997) Model-based and qualitative reasoning: An introduction, in: Annals of Mathematics and Artificial Intelligence 19 Baltzer Science Publication, pp. 355–381.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ariton, V. Handling Qualitative Aspects of Human Knowledge in Diagnosis. J Intell Manuf 16, 615–634 (2005). https://doi.org/10.1007/s10845-005-4366-y
Issue Date:
DOI: https://doi.org/10.1007/s10845-005-4366-y