Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Francisco De Nolasco Santos"'
Autor:
Francisco de Nolasco Santos, Koen Robbelein, Pietro D’Antuono, Nymfa Noppe, Wout Weijtjens, Christof Devriendt
Publikováno v:
Lecture Notes in Civil Engineering ISBN: 9783031072536
In recent years there has been an increased interest of the offshore wind industry to use structural health monitoring (SHM) data in the assessment of consumed lifetime and lifetime extension for an entire wind farm. In order for operators, certifyin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1624e0deb312e8d5e3718fe9dee1dd9c
https://doi.org/10.1007/978-3-031-07254-3_13
https://doi.org/10.1007/978-3-031-07254-3_13
Autor:
Francisco de Nolasco Santos, Pietro D'Antuono, Nymfa Noppe, Wout Weijtjens, Christof Devriendt
Publikováno v:
ESANN 2022 proceedings.
In this contribution we present a physics-informed neural network (PINN) approach for wind turbine fatigue estimation. This PINN incorporates physical information of the structure’s fatigue profile in its loss function, referred to as Minkowski log
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b655dd9b853038db22f25df36323a670
https://doi.org/10.14428/esann/2022.es2022-16
https://doi.org/10.14428/esann/2022.es2022-16
Publikováno v:
Vrije Universiteit Brussel
In this contribution, the strain measurements of a railway bridge are used for anomaly detection, in the context of Structural Health Monitoring (SHM). The methodology used is a combination of a sparse convolutional autoencoder (CSAE) and a Mahalanob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1aff0fd265c6d6dad07db1bb2628e6a0
https://hdl.handle.net/20.500.14017/1d2ae34d-6fe8-473f-afe8-0407be3ef441
https://hdl.handle.net/20.500.14017/1d2ae34d-6fe8-473f-afe8-0407be3ef441
The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7bd3131f1de2b9fa19818359389ca8d2
https://doi.org/10.5194/wes-2021-76
https://doi.org/10.5194/wes-2021-76
Publikováno v:
Journal of Physics: Conference Series. 1618:022020
In this contribution SCADA data and thrust attained through strain measurements are used to train a neural network model which predicts the thrust load of an offshore wind turbine. The model is subsequently cross-validated for different turbines with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2993f80abe2337e6f0dae8382b4d4659
https://doi.org/10.1088/1742-6596/1618/2/022020
https://doi.org/10.1088/1742-6596/1618/2/022020
Publikováno v:
Vrije Universiteit Brussel
As older wind farms begin to reach their design lifetime, topics such as wind turbine fatigue, the remaining useful lifetime and lifetime extensions also start to be discussed with greater frequency. It is in this context that wind turbine fatigue as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0fe77e2c5a8f69152c3c6e36597330dd
https://researchportal.vub.be/en/publications/eb4fa3d5-7063-4728-9332-94aab5ccb5d6
https://researchportal.vub.be/en/publications/eb4fa3d5-7063-4728-9332-94aab5ccb5d6