Abstract
This paper sets out to provide a basis for a specification methodology for modelbased diagnostic systems (MBDS). The purpose of the methodology is to provide a mapping from the problem space of possible diagnostic applications to the solution space provided by the various approaches to MBDS. Therefore, given the major characteristics of a diagnostic problem, the methodology should provide guide-lines by which the specification of a suitable MBDS may be determined. As a first stage in the development of this methodology we provide taxonomies of the problem and solution spaces. The former is characterised via a set ofProblem requirements, divided intoTask, Fault andModel requirements, while the latter is classified as a set ofSystem specifications, with reference to the major functional blocks of MBDS: namely theDiagnostic Strategist, thePredictor and theCandidate Proposer. The last part of this paper proposes a mapping between these two multi-dimensional spaces. This mapping is preliminary, and therefore neither exhaustive nor exclusive, but together with the taxonomies of the problem requirements and system specification will provide a catalyst for the development of a more extensive methodology.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
L. Console and P. Torasso, Hypothetical reasoning in casual models, Int. J. Intelligent Syst. (1990) 83–124.
J. de Kleer, An assumption-based TMS, Artificial Intelligence (1986) 127–162.
J. de Kleer and B.C. Williams, Diagnosing multiple faults, Artificial Intelligence (1987) 97–130.
J. de Kleer and B.C. Williams, Diagnosing with behavioural modes,Proc. 11th Int. Joint Conf. on Artificial Intelligence, Vol. 2 (1989) 1324–1330.
D. Dvorak and B. Kuipers, Model-based monitoring of dynamic systems,Proc. 11th Int. Joint Conf. on Artificial Intelligence, Vol. 2 (1989) pp. 1338–1343.
B.J. Kuipers, Qualitative simulation, Artificial Intelligence (1986) 289–338.
Y. Landau,Adaptive Control: The Model Reference Approach (Marcel Dekker, 1979).
R.R. Leitch, H. Freitag, Q. Shen, P. Struss and G. Tornielli, ARTIST: A methodological approach to specifying model based diagnostic systems, in: G. Guida and A. Stefanini (eds.),Industrial Applications of Knowledge Based Diagnosis (1992).
R.R. Leitch and Q. Shen, Finding faults with model based diagnosis,Proc. 2nd Int. Workshop on Principles of Diagnosis, Milan, 1991, pp. 121–134.
R. Patton, P. Frank and R. Clark (eds.),Fault Diagnosis in Dynamic Systems: Theory and Applications (Prentice Hall, 1989).
R. Reiter, A theory of diagnosis from first principles, Artificial Intelligence (1987) 57–95.
Q. Shen and R.R. Leitch, Diagnosing continuous dynamic systems using qualitative simulation,Proc. 3rd Int. Conf. on Control, Vol. 2 (1991) 1000–1006.
Q. Shen and R.R. Leitch, Application studies of fuzzy qualitative simulation, in: P. Borne and S.G. Tzafestas (eds.),Mathematics of the Analysis and Design of Process Control (Elsevier, 1992).
P. Struss and O. Dressler, Physical negation — Integrating fault models into general diagnostic engine,Proc. 11th Int. Joint Conf. on Artificial Intelligence, Vol. 2 (1989) pp. 1318–1323.
G.J. Sussman and G.L. Steele, Constraints — A language for expressing almost hierarchical descriptions, Artificial Intelligence (1980) 1–39.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Leitch, R.R., Chantler, M.J., Shen, Q. et al. A preliminary specification methodology for model-based diagnosis. Ann Math Artif Intell 11, 11–32 (1994). https://doi.org/10.1007/BF01530735
Issue Date:
DOI: https://doi.org/10.1007/BF01530735