Ponencia
Data Fault Detection for Digital Twin Learning Action Decision of a Wind Turbine
Autor/es | Chicaiza Salazar, William David
Rodríguez Sánchez, Fabio Sánchez, Adolfo J. Escaño González, Juan Manuel |
Departamento | Universidad de Sevilla. Departamento de Matemática Aplicada II (ETSI) Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2022 |
Fecha de depósito | 2023-06-22 |
ISBN/ISSN | 978-84-1319-379-3 |
Resumen | This paper presents the design of a classifier of
variable failures in a wind turbine system. The classifier is based
on a structure formed by several TS fuzzy inference systems, with
projections of the data onto ... This paper presents the design of a classifier of variable failures in a wind turbine system. The classifier is based on a structure formed by several TS fuzzy inference systems, with projections of the data onto components of a principal component analysis. The classifier is part of a discrepancy evaluator for triggering the learning mechanism of the digital twin of the wind turbine. |
Agencias financiadoras | Spanish Ministry of Science, Innovation and Universities under grant PID2019- 104149RB-I00 European Union’s Horizon 2020 grant agreement no. 958339 |
Identificador del proyecto | PID2019- 104149RB-I00
EU H2020 no. 958339 |
Ficheros | Tamaño | Formato | Ver | Descripción |
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rodriguez-sanchez_2022_data.pdf | 1.720Mb | [PDF] | Ver/ | |