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.
How to Cite
diagnosis, fault isolation, diagnosability analysis, dynamic systems
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