Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Christian Tutiven"'
Autor:
Christian Tutivén, Yolanda Vidal, Andres Insuasty, Lorena Campoverde-Vilela, Wilson Achicanoy
Publikováno v:
Energies, Vol 15, Iss 12, p 4381 (2022)
To reduce the levelized cost of wind energy, through the reduction in operation and maintenance costs, it is imperative that the wind turbine downtime is reduced through maintenance strategies based on condition monitoring. The standard approach towa
Externí odkaz:
https://doaj.org/article/fe60cdb8ef2341918e5c3b5ec873ab2f
Publikováno v:
Mathematics, Vol 10, Iss 7, p 1131 (2022)
Offshore wind energy is increasingly being realized at deeper ocean depths where jacket foundations are now the greatest choice for dealing with the hostile environment. The structural stability of these undersea constructions is critical. This paper
Externí odkaz:
https://doaj.org/article/a9013bcb8c3e44eca94e59d71205abc1
Publikováno v:
Sensors, Vol 21, Iss 10, p 3333 (2021)
Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in larg
Externí odkaz:
https://doaj.org/article/59fa82e8f0484c56b6655a3b707043ff
Publikováno v:
Sensors, Vol 21, Iss 6, p 2228 (2021)
As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement
Externí odkaz:
https://doaj.org/article/7317eece3a344ae4bae6682d05635aef
Publikováno v:
Sensors, Vol 20, Iss 12, p 3429 (2020)
This work deals with structural health monitoring for jacket-type foundations of offshore wind turbines. In particular, a vibration-response-only methodology is proposed based on accelerometer data and deep convolutional neural networks. The main con
Externí odkaz:
https://doaj.org/article/0a75eb8f8d9f483f8454b2ac8db008f2
Publikováno v:
Energies, Vol 8, Iss 5, Pp 4300-4316 (2015)
This paper develops a fault diagnosis (FD) and fault-tolerant control (FTC) of pitch actuators in wind turbines. This is accomplished by combining a disturbance compensator with a controller, both of which are formulated in the discrete time domain.
Externí odkaz:
https://doaj.org/article/3b242350fdc84d47bafa1721a39d470b
Publikováno v:
Energies, Vol 11, Iss 11, p 3018 (2018)
Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this
Externí odkaz:
https://doaj.org/article/836fd38f7f54440ba02a395bc029d3d5
Autor:
Bolivar Nunez-Montoya, Carlos Naranjo-Riofrio, Luis Lopez-Estrada, Christian Tutiven, Yolanda Vidal, Marcelo Fajardo-Pruna
Publikováno v:
2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d7655b402cab4ce8d70b7fe5a282dde
https://doi.org/10.1109/ic_aset53395.2022.9765858
https://doi.org/10.1109/ic_aset53395.2022.9765858
Autor:
Arom Moreno-Ortiz, Daniela Sanchez-Orozco, Luis Lopez-Estrada, Christian Tutiven, Yolanda Vidal, Marcelo Fajardo-Pruna
Publikováno v:
2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50bb159428d574dd6b95f8cfd4914bf8
https://doi.org/10.1109/ic_aset53395.2022.9765928
https://doi.org/10.1109/ic_aset53395.2022.9765928
Publikováno v:
Data in Brief, Vol 53, Iss , Pp 110222- (2024)
This dataset provides a comprehensive collection of vibrational data for the purpose of structural health monitoring, particularly focusing on the detection of bolt loosening in offshore wind turbine jacket foundations. The data set comprises 780 com
Externí odkaz:
https://doaj.org/article/b58d4d42fa1a4e6b9a836b7d7a836269