Efficient Generation of Minimal Dynamic Bayesian Networks for Hybrid Systems Fault Diagnosis using Hybrid Possible Conflicts
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Abstract
Hybrid systems diagnosis requires different sets of equations for each operation mode in order to estimate the continuous system behaviour. In this work we rely upon Hybrid Possible Conflicts (HPCs), which are an extension of Possible Conflicts (PCs) for hybrid systems, that introduce the information about potential system modes as control specifications that activate/deactivate different sets of equations. We also introduce the concept of Hybrid Minimal Evaluation Models (H-MEMs) to represent the set of globally consistent causal assignments in an HPC for any potential mode.
H-MEMs can be explored for a specific operation mode, and its computational model automatically generated. In this work, the selected computational models are minimal Dynamic Bayesian Networks (DBNs). Since DBNs can be directly generated from PCs, and can be used for fault detection and isolation, we propose to efficiently generate Mini- mal DBNs models on-line using the H-MEM structure. By introducing fault parameters in the DBN model, we can also perform fault identification, providing an unifying framework for fault diagnosis, under single fault assumption. We test the approach in a simulation four-tank system.
How to Cite
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fault diagnosis, Hybrid Systems, Model-based diagnosis, Dynamic Bayesian Network, Possible Conflicts
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