Efficient Generation of Minimal Dynamic Bayesian Networks for Hybrid Systems Fault Diagnosis using Hybrid Possible Conflicts

Belarmino Pulido, Noemi Moya, Carlos J. Alonso-Gonzalez, and Anibal Bregon
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Spanish Ministry of Science and Innovation (now Minneco)
phmc_13_044.pdf847.56 KBOctober 8, 2013 - 9:16am

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 Minimal 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.

Publication Year: 
Publication Volume: 
Publication Control Number: 
Page Count: 
Submission Keywords: 
Model-based diagnosis
Hybrid Systems
Dynamic Bayesian Network
fault diagnosis
Possible Conflicts
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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