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Structural Diagnosability Analysis of Dynamic Models

This work is focused on structural approaches to studying diagnosability properties given a system model taking into account, both simultaneously or separately, integral and differential causal interpretations for differential constraints. We develop a model characterization and corresponding algorithms, for studying system diagnosability using a structural decomposition that avoids generating the full set of system ARRs. Simultaneous application of integral and differential causal interpretations for differential constraints results in a mixed causality interpretation for the system. The added power of mixed causality is demonstrated using a case study. Finally, we summarize our work and provide a discussion of the advantages of mixed causality over just derivative or just integral causality.

Jan Åslund, Anibal Bregon, Mattias Krysander, Erik Frisk, Belarmino Pulido and Gautam Biswas


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Senast uppdaterad: 2019-03-29