Data-driven techniques for the fault diagnosis of a wind turbine benchmark

DOI10.2478/amcs-2018-0018zbMath1396.93084OpenAlexW2883388344WikidataQ129499675 ScholiaQ129499675MaRDI QIDQ1784047

Silvio Simani, Paolo Castaldi, Saverio Farsoni

Publication date: 26 September 2018

Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2478/amcs-2018-0018

zbMATH Keywords

neural networksfault diagnosisresidual generatorsfuzzy systemsfault estimationanalytical redundancywind turbine benchmark

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Learning and adaptive systems in artificial intelligence (68T05) Fuzzy control/observation systems (93C42) Application models in control theory (93C95)

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