TY - JOUR
T1 - Review of robot-based damage assessment for offshore wind turbines
AU - Liu, Y.
AU - Hajj, M.
AU - Bao, Y.
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Automated detection
KW - Computer vision
KW - Damage assessment
KW - Machine learning
KW - Nondestructive evaluation
KW - Offshore wind turbines
KW - Robots
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U2 - 10.1016/j.rser.2022.112187
DO - 10.1016/j.rser.2022.112187
M3 - Review article
AN - SCOPUS:85123642957
SN - 1364-0321
VL - 158
JO - Renewable and Sustainable Energy Reviews
JF - Renewable and Sustainable Energy Reviews
M1 - 112187
ER -