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- W2185304841 abstract "It is well known the importance of fault diagnosis, systems have to be working in a safe and reliable mode. The way to keep systems working properly is not only to detect when a fault occurs, but also where the fault has occurred as well as its magnitude. Model-based diagnosis techniques have been successfully used for fault diagnosis tasks. However, model-based diagnosis techniques faced some problems when dealing with continuous systems governed by discrete events. Continuous systems governed by digital controllers appear frequently in embedded systems. The work developed with those systems, using a hybrid automata modeling formalism, shows the complexity of tracking all different modes and possible faults. The dissertation presented in this paper proposes a more efficient way to perform fault diagnosis in hybrid systems using Dynamic Bayesian Networks and Possible Conflicts. 1. OUTLINE OF THE THESIS" @default.
- W2185304841 created "2016-06-24" @default.
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- W2185304841 date "2011-01-01" @default.
- W2185304841 modified "2023-09-27" @default.
- W2185304841 title "Fault Detection and Isolation in Hybrid Systems with Dynamic Bayesian Networks and Consistency Based Diagnosis" @default.
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