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 manage and prepare to enter the diagnosis system without the help of an appropriate tool. The paper proposes a knowledge elicitation scheme for multifunctional conductive flow systems’ faulty behaviour, along with appropriate representation of instance manifestations in a semi-qualitative manner, suited to human diagnostician conceptual view. Computer Aided Knowledge Elicitation (CAKE) tool proposed copes with knowledge involved in diagnosis. A case study on a hydraulic installation is finally presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ariton, V.: Deep and shallow knowledge in fault diagnosis. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 748–755. Springer, Heidelberg (2003)
Cellier, F.E.: Modeling from Physical Principles. In: Levine, W.S. (ed.) The Control Handbook, pp. 98–108. CRC Press, Boca Raton (1995)
Larsson, J.E.: Knowledge-based methods for control systems, PhD Thesis, Lund (1992)
Mosterman, P.J., Kapadia, R., Biswas, G.: Using bond graphs for diagnosis of dynamical physical systems. In: Sixth Intl. Conference on Principles of Diagnosis, Goslar, Germany, pp. 81–85 (1995)
Kruse, R., Gebhardt, J.: Foundations of fuzzy systems. John Wiley & Sons, Chichester (1994)
Kuipers, B.J.: Qualitative reasoning: modeling and simulation with incomplete knowledge. MIT Press, Cambridge (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ariton, V. (2004). Handling Qualitative Aspects of Human Knowledge in Diagnosis. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_49
Download citation
DOI: https://doi.org/10.1007/978-3-540-30133-2_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23206-3
Online ISBN: 978-3-540-30133-2
eBook Packages: Springer Book Archive