Assessing damage to wind turbine blades to support autonomous inspection

被引:0
|
作者
Gibson, Andy [1 ]
Simandjuntak, Sarinova [2 ]
Dunkason, Emily [1 ]
Bingari, Hanly [1 ]
Fraess-Ehrfeld, Alex [3 ]
机构
[1] Univ Portsmouth, Ctr Appl Geosci Portsmouth, Portsmouth, Hants, England
[2] Anglia Ruskin Univ, Sch Engn & Built Environm, Chelmsford, Essex, England
[3] Airborne Robot Ltd, 31 Thurloe St, London, England
来源
基金
“创新英国”项目;
关键词
Wind turbine; autonomous; drone; offshore; hyperspectral; damage; inspection; InnovateUK;
D O I
10.1117/12.2644972
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We describe results from experiments investigating how hyperspectral data might be incorporated into autonomous inspections for offshore turbines, part of Dr SUIT- (Drone Swarm for Unmanned Inspection of Wind Turbines), a collaboration funded by InnovateUK (UKRI). Imagery and point measurements were captured of small turbine blades subjected to damage by abrasion, impact and UV exposure. The technique appears effective at classifying abrasion damage to a degree comparable with conventional inspection schemes. Impact damage could be classified as 'lower' or 'higher' energies. The blades designed resilience to UV meant that little change was detected in those tests.
引用
收藏
页数:7
相关论文
共 50 条
  • [31] Damage detection of wind turbine blades by Bayesian multivariate cointegration
    Xu, Mingqiang
    Li, Jun
    Wang, Shuqing
    Yang, Ning
    Hao, Hong
    OCEAN ENGINEERING, 2022, 258
  • [32] Machine Learning Techniques for Damage Detection in Wind Turbine Blades
    Tavares, Andre
    Lopes, Bernardo
    Di Lorenzo, Emilio
    Cornelis, Bram
    Peeters, Bart
    Desmet, Wim
    Gryllias, Konstantinos
    EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 1, 2023, 253 : 176 - 189
  • [33] Damage identification of wind turbine blades based on acoustic emission
    Wang, Zihan
    Xu, Kaidi
    Luo, Zhichun
    Zhang, Jia
    Bi, Yanzhao
    INSIGHT, 2022, 64 (05) : 279 - 284
  • [34] Image-based Damage Recognition of Wind Turbine Blades
    Yu, Yajie
    Cao, Hui
    Liu, Shang
    Yang, Shuo
    Bai, Ruixian
    2017 2ND INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM), 2017, : 161 - 166
  • [35] A coupled aeroelastic damage progression model for wind turbine blades
    Cardenas, Diego
    Elizalde, Hugo
    Marzocca, Piergiovanni
    Gallegos, Sergio
    Probst, Oliver
    COMPOSITE STRUCTURES, 2012, 94 (10) : 3072 - 3081
  • [36] Simulation of Damage for Wind Turbine Blades Due to Airborne Particles
    Fiore, Giovanni
    Selig, Michael S.
    WIND ENGINEERING, 2015, 39 (04) : 399 - 418
  • [37] Assessing Fatigue Damage in the Reuse of a Decommissioned Offshore Jacket Platform to Support a Wind Turbine
    Heo, Taemin
    Liu, Ding Peng
    Manuel, Lance
    Correia, Jose A. F. O.
    Mendes, Paulo
    JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME, 2023, 145 (04):
  • [38] Harnessing the power of thermal imagery and visual inspection-a mean for reliable damage detection of wind turbine rotor blades
    Sridaran Venkat, Ramanan
    Stamm, Michael
    Wittmann, Jost
    Lauterbach, Helge
    Bleier, Michael
    e-Journal of Nondestructive Testing, 2024, 29 (07):
  • [39] LiDAR-based automated UAV inspection of wind turbine rotor blades
    Wembers, Carlos Castelar
    Pflughaupt, Jasper
    Moshagen, Ludmila
    Kurenkov, Michael
    Lewejohann, Tim
    Schildbach, Georg
    JOURNAL OF FIELD ROBOTICS, 2024, 41 (04) : 1116 - 1132
  • [40] Comparison of nondestructive testing techniques for the inspection of wind turbine blades' spar caps
    Martin, Robert W.
    Sabato, Alessandro
    Schoenberg, Andrew
    Giles, Robert H.
    Niezrecki, Christopher
    WIND ENERGY, 2018, 21 (11) : 980 - 996