Model-based support for mutable parametric design optimization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
MDS: An Integrated Architecture for Associational and Model-Based Diagnosis
Applied Intelligence
Model-Based Diagnosis and Conditional Logic
Applied Intelligence
The Knowledge Engineering Review
SymCure: a model-based approach for fault management with causal directed graphs
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Diagnosis as a variable assignment problem: a case study in a space robot fault diagnosis
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Monitoring, prediction, and fault isolation in dynamic physical systems
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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This paper discusses systematic methods for diagnosis of complex engineering systems combining qualitative and quantitative analysis of analytic constraint equation system models to generate more precise and accurate candidates. Candidates generated from a qualitative steady-state partial explanation model are refined with available ordinal information on the magnitude of component parameter deviations. In addition, an incremental algorithm is implemented to efficiently process sequences of measurements. Empirical analysis demonstrates that accuracy and resolution of minimal candidate generation are improved by including ordinal information. This avoids the practical problems encountered when reasoning with pure quantitative information