Abstract
Diagnosis of real world problems demands the integration of different techniques from several research fields. In Model-based Diagnosis, both Artificial Intelligence and Control Theory communities have provided different but complementary approaches. Recent works, known as BRIDGE proposal, provided a common framework for the integration of techniques for static systems.
This work proposes the extension of the BRIDGE framework for a specific class of dynamic systems, thus analyzing the influence of dynamic constraints in the behavior estimation capabilities for two Model-based Diagnosis techniques: Possible Conflicts and Analytical Redundancy Relations obtained through structural analysis. Results show the strong similarities between them, and provide new ways for integration of techniques from both areas. Additionally, algorithms computing Possible Conflicts provide the implicit structural model for state observer design with no extra knowledge added in the model. Results on a case study are provided, then compared and discussed against existing proposals.
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Pulido, B., Bregón, A., Alonso-González, C.J. (2010). Analyzing the Influence of Differential Constraints in Possible Conflict and ARR Computation. In: Meseguer, P., Mandow, L., Gasca, R.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2009. Lecture Notes in Computer Science(), vol 5988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14264-2_2
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DOI: https://doi.org/10.1007/978-3-642-14264-2_2
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