Name
Abstract | ||
|---|---|---|
We describe an approach for modeling and diagnosing complex multi-modal systems. A novel feature of this approach is the proposed method for integrating multiple specification modalities. We apply this approach to a real-world rocket engine turbopump. The example model consists of two submodels, one describing liquid flows, and one describing mechanical vibrations. Typical methods for modeling such systems will treat these two modalities as being completely disjoint, even though they have significant interactions. We describe a method for integrating these sub-models using variables that play different roles in the different sub-models. We focus on how the integrated causal network model allows us to diagnose a more complete and physically plausible set of faults than by using the independent sub-models. |
Year | Venue | Field |
|---|---|---|
1999 | AI Commun. | Modalities,Disjoint sets,Computer science,Turbopump,Artificial intelligence,Vibration,Rocket engine,Network model,Modal |
DocType | Volume | Issue |
Journal | 12 | 1-2 |
ISSN | Citations | PageRank |
0921-7126 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| gregory provan | 1 | 503 | 120.02 |
| David Glover | 2 | 0 | 0.34 |