Review of robot-based damage assessment for offshore wind turbines

Y. Liu, M. Hajj and Y. Bao

Renewable and Sustainable Energy Reviews, 2022, vol. 158, issue C

Abstract: 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.

Keywords: Automated detection; Computer vision; Damage assessment; Machine learning; Nondestructive evaluation; Offshore wind turbines; Robots (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.rser.2022.112187

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