Abstract
Despite ample advantages of model-based diagnosis, in practice its use has been somehow limited to proof-of-concept prototypes. Some reasons behind this observation are that the required modeling step is resource consuming, and also that this step requires additional training. In order to overcome these problems, we suggest to use modeling languages like Modelica that are already established in academia and industry for describing cyber-physical systems as basis for deriving logic based models. Together with observations about the modeled system, those models can then be used by an abductive diagnosis engine for deriving the root causes for detected defects. The idea behind our approach is to introduce fault models for the components written in Modelica, and to use the available simulation environment to determine behavioral deviations to the expected outcome of a fault free model. The introduced fault models and gained information about the resulting deviations can be directly mapped to horn clauses to be used for diagnosis.
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Acknowledgement
The work presented in this paper has been supported by the FFG project Applied Model Based Reasoning (AMOR) under grant 842407. We would further like to express our gratitude to our industrial partner, Uptime Engineering GmbH.
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Peischl, B., Pill, I., Wotawa, F. (2016). Using Modelica Programs for Deriving Propositional Horn Clause Abduction Problems. In: Friedrich, G., Helmert, M., Wotawa, F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science(), vol 9904. Springer, Cham. https://doi.org/10.1007/978-3-319-46073-4_18
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DOI: https://doi.org/10.1007/978-3-319-46073-4_18
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