Arrow Research search
Back to AAAI

AAAI 1997

Monitoring, Prediction, and Fault Isolation in Dynamic Physical Systems

Conference Paper Diagnosis Artificial Intelligence

Abstract

‘Diagnosis of dynamic physical systems is complex and requires close interaction of monitoring, fault generation and refinement, and prediction. We establish a methodology for model-based diagnosis of continuous systems in a qualitative reasoning framework. A temporal causal model capturing dynamic system behavior identifies faults from deviant measurements and predicts future system behavior expressed as signatures, i. e. , qualitative magnitude changes and higher order time-derivative effects. A comparison of the transient characteristics of the observed variables with the predicted effects helps refine initial fault hypotheses. This shows for quick fault isolation, and circumvents difficulties that arise when interactions caused by feedback and dependent faults. This methodology has been successfully applied to the secondary cooling loop of fast breeder reactors.

Authors

Keywords

No keywords are indexed for this paper.

Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
690148038358968499