Name
Abstract | ||
|---|---|---|
Tasks like diagnosis, failure-modes-and-effects analysis (FMEA), and therapy proposal involve reasoning about variables and parameters deviating from some reference state. In model-based systems, one tries to capture this kind of inferences by models that describe how such deviations are emerging and propagated through a system. Several techniques and systems have been developed that address this issue, in particular in the area of qualitative modeling. However, to our knowledge, a rigorous mathematical foundation and a "recipe" for how to construct such compositional deviation models has not been presented in the literature, despite the widespread use of the idea and the techniques. In this paper, we present a general mathematical formalization of deviation models. Based on this, aspects of constructing libraries of deviation models, their properties, and their application in consistency-based diagnosis and prediction-based FMEA in a component-oriented framework are analyzed. |
Year | Venue | Keywords |
|---|---|---|
2004 | FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS | failure mode and effect analysis |
Field | DocType | Volume |
Computer science,Artificial intelligence,Recipe,Machine learning | Conference | 110 |
ISSN | Citations | PageRank |
0922-6389 | 3 | 0.56 |
References | Authors | |
2 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Struss | 1 | 365 | 52.90 |