Detecting changes in signals and systems—a survey
Automatica (Journal of IFAC)
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Conditional independence and its representations
Readings in uncertain reasoning
IEEE Internet Computing
An Architecture for Online Diagnosis of Gas Turbines
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
KNOWLEDGE-BASED VALIDATION FOR HYDROLOGICAL INFORMATION SYSTEMS
Applied Artificial Intelligence
Explaining inferences in Bayesian networks
Applied Intelligence
Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns
ACM Transactions on Sensor Networks (TOSN)
Any time probabilistic reasoning for sensor validation
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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The validation of data from sensors has become an important issue in the operation and control of modern industrial plants. One approach is to use knowledge based techniques to detect inconsistencies in measured data. This article presents a probabilistic model for the detection of such inconsistencies. Based on probability propagation, this method is able to find the existence of a possible fault among the set of sensors. That is, if an error exists, many sensors present an apparent fault due to the propagation from the sensor(s) with a real fault. So the fault detection rueehanism can only tell if a sensor has a potential fault, but it can not tell if the fault is real or apparent. So the central problem is to develop a theory, and then an algorithm, for distinguishing real and apparent faults, given that one or more sensors can fail at the same time. This article then, presents an approach based on two levels: (i) probabilistic reasoning, to detect a potential fault, and (ii) constraint management, to distinguish the real fault from the apparent ones. The proposed approach is exemplified by applying it to a power plant model.