Review on Artificial Intelligence-aided Life Extension Assessment of Offshore Wind Support Structures

The primary objective of the present literature review is to provide a constructive and systematical discussion based on the relevant development, unsolved issues, gaps, and misconceptions in the literature regarding the fields of study that are building blocks of artificial intelligence-aided life...

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Bibliographic Details
Published in:Journal of marine science and application 2022-12, Vol.21 (4), p.26-54
Main Authors: Yeter, B., Garbatov, Y., Soares, C. Guedes
Format: Article
Language:English
Subjects:
Artificial intelligence
Condition monitoring
Corrosion
Corrosion fatigue
Crack propagation
Digital twins
Electrical Machines and Networks
Engineering
Failure mechanisms
Fatigue failure
Geotechnical Engineering & Applied Earth Sciences
High cycle fatigue
Life extension
Literature reviews
Low cycle fatigue
Machine learning
Machinery and Machine Elements
Offshore
Offshore Engineering
Offshore structures
Power Electronics
Review
Signal processing
Structural health monitoring
Turbine engines
Turbines
Ultimate tensile strength
Unsupervised learning
Wind power
Wind turbines
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Summary:The primary objective of the present literature review is to provide a constructive and systematical discussion based on the relevant development, unsolved issues, gaps, and misconceptions in the literature regarding the fields of study that are building blocks of artificial intelligence-aided life extension assessment for offshore wind turbine support structures. The present review aims to set up the needed guidelines to develop a multi-disciplinary framework for life extension management and certification of the support structures for offshore wind turbines using artificial intelligence. The main focus of the literature review centres around the intelligent risk-based life extension management of offshore wind turbine support structures. In this regard, big data analytics, advanced signal processing techniques, supervised and unsupervised machine learning methods are discussed within the structural health monitoring and condition-based maintenance planning, the development of digital twins. Furthermore, the present review discusses the critical failure mechanisms affecting the structural condition, such as high-cycle fatigue, low-cycle fatigue, fracture, ultimate strength, and corrosion, considering deterministic and probabilistic approaches.
ISSN:1671-9433
1993-5048
DOI:10.1007/s11804-022-00298-3