Neural Network based Active Fault Diagnosis with a Statistical Test
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43970378" target="_blank" >RIV/49777513:23520/23:43970378 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-35170-9_21" target="_blank" >https://doi.org/10.1007/978-3-031-35170-9_21</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-35170-9_21" target="_blank" >10.1007/978-3-031-35170-9_21</a>
Alternative languages
Result language
angličtina
Original language name
Neural Network based Active Fault Diagnosis with a Statistical Test
Original language description
The paper focuses on designing an active fault detector (AFD) for a nonlinear stochastic system subject to abrupt faults. The neural network (NN) based models of the monitored system and their prediction error uncertainties are identified using historical input-output data obtained from the system under fault-free and all considered faulty conditions. The fault detector is based on a multiple hypothesis CUSUM-like statistical test that uses the identified NN models. The quality of decisions provided by such a detector is improved by a closed loop input signal generator. The input signal generator is represented by another NN and it is designed using a reinforcement learning method. The proposed AFD is illustrated by means of a numerical example.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
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)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 21st Polish Control Conference, PCC 2023
ISBN
978-3-031-35169-3
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
10
Pages from-to
227-236
Publisher name
Springer
Place of publication
Gliwice, Polsko
Event location
Gliwice, Polsko
Event date
Jun 26, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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