Damage assessment of wind turbine blade under static loading test using acoustic emission

被引:30
作者
Han, Byeong-Hee [1 ,2 ]
Yoon, Dong-Jin [1 ]
Huh, Yong-Hak [3 ]
Lee, Young-Shin [2 ]
机构
[1] Korea Res Inst Stand & Sci, Ctr Safety Measurements, Taejon 305340, South Korea
[2] Chungnam Natl Univ, Dept Mech Engn, Taejon, South Korea
[3] Korea Res Inst Stand & Sci, Ctr Energy & Mat Standard, Taejon 305340, South Korea
关键词
source location; composite materials; Wind turbine blade; acoustic emission; nondestructive evaluation; STRUCTURAL NEURAL SYSTEM; SOURCE LOCATION; COMPOSITE;
D O I
10.1177/1045389X13508329
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Acoustic emission is known as a powerful nondestructive tool to detect any further growth of preexisting cracks or to characterize failure mechanisms. Recently, this kind of technique, which is an in situ monitoring of integrity of materials or structures, becomes increasingly popular for monitoring the conditions of large structures such as a wind turbine blade. Therefore, it is required to find a symptom of damage progress before catastrophic failure through a continuous monitoring. In this study, acoustic emission technology was applied to assess the damage in the wind turbine blade during step-by-step static load test. In this static loading test, we have used a full-scale blade of 100 kW in capacity, and an attempt was made to apply a new source location method using a new algorithm with energy contour mapping concept. We also measured the deflection of blade tip by linear variable differential transformer (LVDT) and the strain of inner shear web in order to analyze the correlation between stress condition and damage identification. The results show that the acoustic emission activities give a good agreement with the stress distribution and damage location in the blade, especially in bonding edges around 1000-1500 mm far from the root. Finally, the applicability of new source location method was confirmed by comparison of the result of source location and experimental damage location.
引用
收藏
页码:621 / 630
页数:10
相关论文
共 19 条
[1]  
[Anonymous], 2016, NATL RENEWABLE ENERG
[2]  
Beattie A.G., 1997, P AIAA AEROSPACE SCI, P239
[3]  
Borum K.K., 2006, Proceedings of the 27th Riso International Symposium on Materials Science: polymer composite materials for wind power turbines, P139
[4]   Acoustic Emission Source Location in Unidirectional Carbon-Fiber-Reinforced Plastic Plates with Virtually Trained Artificial Neural Networks [J].
Caprino, G. ;
Lopresto, V. ;
Leone, C. ;
Papa, I. .
JOURNAL OF APPLIED POLYMER SCIENCE, 2011, 122 (06) :3506-3513
[5]   Structural health monitoring for a wind turbine system: a review of damage detection methods [J].
Ciang, Chia Chen ;
Lee, Jung-Ryul ;
Bang, Hyung-Joon .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2008, 19 (12)
[6]   Health monitoring of FRP using acoustic emission and artificial neural networks [J].
de Oliveira, R. ;
Marques, A. T. .
COMPUTERS & STRUCTURES, 2008, 86 (3-5) :367-373
[7]  
Dong-Jin Yoon, 1990, Journal of Acoustic Emission, V9, P237
[8]  
Flemming M.L., 2003, INT C LIGHTNING STAT, V36, P1
[9]   Structural health monitoring techniques for wind turbine blades [J].
Ghoshal, A ;
Sundaresan, MJ ;
Schulz, MJ ;
Pai, PF .
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2000, 85 (03) :309-324
[10]   Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers [J].
Godin, N ;
Huguet, S ;
Gaertner, R ;
Salmon, L .
NDT & E INTERNATIONAL, 2004, 37 (04) :253-264