Approximate Bayesian State Estimation for Active Fault Diagnosis of Large-Scale Systems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969682" target="_blank" >RIV/49777513:23520/23:43969682 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION52260.2023.10224216" target="_blank" >https://doi.org/10.23919/FUSION52260.2023.10224216</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION52260.2023.10224216" target="_blank" >10.23919/FUSION52260.2023.10224216</a>

Result language
angličtina
Original language name
Approximate Bayesian State Estimation for Active Fault Diagnosis of Large-Scale Systems
Original language description
Active fault diagnosis (AFD) of stochastic large-scale systems in multiple model framework involves two stages: offline and online. In the offline stage, an excitation input generator is designed based on a Bellman function. In the online stage, the generator is utilized together with an estimator of the model indices. A similar estimator is used in the offline stage for the Bellman function calculation using the value iteration technique. However, due to the high dimensions of information states of the associated perfect state information problem, the estimator in the offline stage must involve approximations. The paper provides the relations for the estimate calculation using the Bayesian recursive relations, proposes four algorithms, and studies effects of such approximations on the AFD decisions. In particular, the quality of the model index estimates is analyzed using a power network model.
Czech name
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Czech description
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Project
<a href="/en/project/GA22-11101S" target="_blank" >GA22-11101S: Tensor Decomposition in Active Fault Diagnosis for Stochastic Large Scale Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2023 26th International Conference on Information Fusion, FUSION 2023
ISBN
979-8-89034-485-4
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
IEEE
Place of publication
Charleston, Jižní Karolína, USA
Event location
Charleston, Jižní Karolína, USA
Event date
Jun 27, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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