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：

HYPERSPECTRAL IMAGING AND APPLICATIONS II | 2022年 / 12338卷

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.

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页数：7

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