Review of robot-based damage assessment for offshore wind turbines

被引:43
|
作者
Liu, Y. [1 ]
Hajj, M. [1 ]
Bao, Y. [1 ]
机构
[1] Stevens Inst Technol, Dept Civil Environm & Ocean Engn, Hoboken, NJ 07030 USA
来源
关键词
Automated detection; Computer vision; Damage assessment; Machine learning; Nondestructive evaluation; Offshore wind turbines; Robots; CRACK DETECTION; ACOUSTIC-EMISSION; CLIMBING ROBOTS; INSPECTION; SURFACE; SYSTEM; THERMOGRAPHY; TECHNOLOGIES; RECOGNITION; CHALLENGES;
D O I
10.1016/j.rser.2022.112187
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Offshore wind turbines are subjected to highly-varying dynamic loadings and accelerated material degradation, resulting in the need for structural health monitoring, which increases the operation and maintenance cost and ultimately the levelized cost of electricity. Recent advances in robotics and intelligent algorithms offer new opportunities for automated damage assessment that would minimize these costs. This review aims to establish a holistic understanding of robot-based damage assessment technologies and to promote the development and application of these technologies for automated condition assessment of offshore wind turbines. It covers robots as potential carriers of inspection devices, damage inspection approaches, and intelligent algorithms for damage detection, classification, localization, and quantification for offshore wind turbines. The robots include climbing and underwater varieties, and unmanned aerial vehicles, which carry optical and infrared cameras, and X-ray equipment. Advanced machine learning algorithms for analysis of inspection data are evaluated. Challenges and opportunities of robot-based damage assessment technologies are discussed.
引用
收藏
页数:15
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