Digital Library

of the European Council for Modelling and Simulation



Pruning Procedure For Incomplete-Observation Diagnostic

Model Simplification


Ivan Havel

Published in:


ECMS 2007 Proceedings

Edited by: Ivan Zelinka, Zuzana Oplatkova, Alessandra Orsoni


ISBN: 978-0-9553018-2-7

Doi: 10.7148/2007


21st European Conference on Modelling and Simulation,

Prague, June 4-6, 2007


Citation format:

Havel, I. (2007). Pruning Procedure For Incomplete-Observation Diagnostic Model Simplification. ECMS 2007 Proceedings edited by: I. Zelinka, Z. Oplatkova, A. Orsoni (pp. 515-520). European Council for Modeling and Simulation. doi:10.7148/2007-0515.



Model-based diagnostics deals with diagnosing systems, it means determining health of system com- ponents based on a system description and an obser- vation of system variables. This paper focuses on sys- tems that can be described in propositional logic, par- ticularly on simplification of their diagnostic models for some given conditions of observation. It is known that performing diagnostics on the entire model of a system when only few variables are expected to be observed is not efficient. If we knew the limited set of variables which may appear in the observation then they would be used to simplify the diagnostic model before the diag- nosis inference takes place. A pruning procedure which systematically removes segments of a model that do not contribute to the overall system diagnosis is proposed. The procedure employs an algorithm deciding compo- nent diagnosability which is based on directional resolu- tion. Diagnoses retrieved using the simplified model are equivalent to those retrieved using the original model.

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