Diagnostic reasoning based on structure and behavior
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Artificial Intelligence
Machine Learning
Expert Systems for Monitoring and Control
Expert Systems for Monitoring and Control
QPC: a compiler from physical models into qualitative differential equations
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Obtaining quantitative predictions from monotone relationships
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Formalizing reasoning about change: a qualitative reasoning approach
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Numerical behavior envelopes for qualitative models
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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
Qualitative modeling as a paradigm for diagnosis and prediction in critical care environments
Artificial Intelligence in Medicine
Guardian: A prototype intelligent agent for intensive-care monitoring
Artificial Intelligence in Medicine
On the soundness and safety of expert systems
Artificial Intelligence in Medicine
Intermediate depth representations
Artificial Intelligence in Medicine
Model-based diagnosis in intensive care monitoring: The YAQ approach
Artificial Intelligence in Medicine
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Industrial process plants such as chemical refineries and electric power generation are examples of continuous-variable dynamic systems (CVDS) whose operation is continuously monitored for abnormal behavior. CVDSs pose a challenging diagnostic problem in which values are continuous (not discrete), relatively few parameters are observable, parameter values keep changing, and diagnosis must be performed while the system operates. We present a novel method for monitoring CVDSs which exploits the system's dynamic behavior for diagnostic clues. The key techniques are: modeling the physical system with dynamic qualitative/quantitative models, inducing diagnostic knowledge from qualitative simulations, continuously comparing observations against fault-model predictions, and incrementally creating and testing multiple-fault hypotheses. The important result is that the diagnosis is refined as the physical system's dynamic behavior is revealed over time.