Path: Top -> Journal -> Telkomnika -> 2014 -> Vol 12, No 3: September
Failure Mechanism Analysis and Failure Number Prediction of Wind Turbine Blades
Failure Mechanism Analysis and Failure Number Prediction of Wind Turbine Blades
Journal from gdlhub / 2016-11-12 08:13:03Oleh : Yu Chun-yu, Guo Jian-ying, Xin Shi-guang, Telkomnika
Dibuat : 2014-09-01, dengan 1 file
Keyword : Wind turbine; blades; fault number; grey model; failure mechanism
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/76
Pertinent to the problems that wind turbine blades operate in complicated conditions, frequent failures and low replacement rate as well as rational inventory need, this paper, we build a fault tree model based on in-depth analysis of the failure causes. As the mechanical vibration of the wind turbine takes place first on the blades, the paper gives a detailed analysis to the Failure mechanism of blade vibration. Therefore the paper puts forward a dynamic prediction model of wind turbine blade failure number based on the grey theory. The relative error between its prediction and the field investigation data is less than 5%, meeting the actual needs of engineering and verifying the effectiveness and applicability of the proposed algorithm. It is of important engineering significance for it to provide a theoretical foundation for the failure analysis, failure research and inventory level of wind turbine blades.
Deskripsi Alternatif :Pertinent to the problems that wind turbine blades operate in complicated conditions, frequent failures and low replacement rate as well as rational inventory need, this paper, we build a fault tree model based on in-depth analysis of the failure causes. As the mechanical vibration of the wind turbine takes place first on the blades, the paper gives a detailed analysis to the Failure mechanism of blade vibration. Therefore the paper puts forward a dynamic prediction model of wind turbine blade failure number based on the grey theory. The relative error between its prediction and the field investigation data is less than 5%, meeting the actual needs of engineering and verifying the effectiveness and applicability of the proposed algorithm. It is of important engineering significance for it to provide a theoretical foundation for the failure analysis, failure research and inventory level of wind turbine blades.
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