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
2011
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
LiU Homepage
