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Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Logic programming for robot control
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
On fuzzy logic applications for automatic control, supervision, and fault diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Integration of Fault Detection and Diagnosis in a Probabilistic Logic Framework
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
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In this paper we present an approach to detect and diagnose multiple faults in industrial processes with a hybrid multiagent diagnostic system. We integrate artificial intelligence model-based diagnosis with control systems Fault Detection and Isolation (FDI) techniques. We adapt a probabilistic logic framework to perform fault detection and diagnosis tasks. The whole diagnosis task is performed by agents and is executed in two phases. In first phase, the Alarm Processor (AP) agent processes the discrete observations and alarms, and outputs a set of most likely faulted components. In second phase, Fault Detection (FD) agents discard the components not participating in the failure, by analyzing a set of continuous signals, that have a different behavior in normal and in faulty state. The FD agents include dynamic probabilistic models, able to deal with noise, nonlinear behavior and missing data. The output of the diagnostic system includes the components with abnormal behavior and the type of faults. We have tested our approach by diagnosing faults in a simulated electrical power network.