A review of challenges and framework development for corrosion fatigue life assessment of monopile-supported horizontal-axis offshore wind turbines

Okenyi, Victor Adejo; Bodaghi, Mahdi; Mansfield, Neil J.; Afazov, Shukri; Siegkas, Petros

doi:10.1080/17445302.2022.2140531

Cited by 12 publications

(5 citation statements)

References 115 publications

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“…Literature also showcases applications more focused on fault detection. Okenyi et al (2022) delve into the application of ANN within digital twin systems for offshore wind turbines, particularly highlighting their efficacy in fault detection. They emphasize that ANN, alongside other ML techniques, excels in tracing patterns in fault diagnosis, crucial for addressing challenges in corrosion fatigue assessment.…”

Section: Module 4: Ai-driven Diagnosis and Remedial Action Recommenda...mentioning

confidence: 99%

Advancing digital healthcare engineering for aging ships and offshore structures: an in-depth review and feasibility analysis

Sindi,

Kim,

Yang

et al. 2024

DCE

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Aging ships and offshore structures face harsh environmental and operational conditions in remote areas, leading to age-related damages such as corrosion wastage, fatigue cracking, and mechanical denting. These deteriorations, if left unattended, can escalate into catastrophic failures, causing casualties, property damage, and marine pollution. Hence, ensuring the safety and integrity of aging ships and offshore structures is paramount and achievable through innovative healthcare schemes. One such paradigm, digital healthcare engineering (DHE), initially introduced by the final coauthor, aims at providing lifetime healthcare for engineered structures, infrastructure, and individuals (e.g., seafarers) by harnessing advancements in digitalization and communication technologies. The DHE framework comprises five interconnected modules: on-site health parameter monitoring, data transmission to analytics centers, data analytics, simulation and visualization via digital twins, artificial intelligence-driven diagnosis and remedial planning using machine and deep learning, and predictive health condition analysis for future maintenance. This article surveys recent technological advancements pertinent to each DHE module, with a focus on its application to aging ships and offshore structures. The primary objectives include identifying cost-effective and accurate techniques to establish a DHE system for lifetime healthcare of aging ships and offshore structures—a project currently in progress by the authors.

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Section: Module 4: Ai-driven Diagnosis and Remedial Action Recommenda...mentioning

confidence: 99%

Advancing digital healthcare engineering for aging ships and offshore structures: an in-depth review and feasibility analysis

Sindi,

Kim,

Yang

et al. 2024

DCE

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“…2. A strain gauge capable of recording a high sample rate was connected to the InstruNET [19] data acquisition system. For each test, a sample rate of 1666.7 samples/secs and a two-flute cutting tool with a diameter of 10 mm were used.…”

Section: Verification Of Fatigue Testing Using Millingmentioning

confidence: 99%

Fatigue Testing Approach Utilising Machining Cutting Forces and Fixture Design

Okenyi,

Afazov,

Mansfield

et al. 2024

Exp Mech

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Background Traditional fatigue testing methods can be expensive due to the need of specialised equipment for engineering materials and structures. Thus, a new fatigue testing approach utilising machining cutting forces to induce cyclic stresses, enabling fatigue life assessment of engineering materials and structures, has been developed. Objective This research aims to develop and verify a new testing approach using machining processes to enable the fatigue life assessment of engineering materials and structures. This is achieved by the utilisation of machining-induced cutting forces to generate cyclic stresses into welded samples used in applications of wind turbine monopile structures. Methods The methodology employes the development of a fixture encompassed with strain gauges and purposefully designed machining operations to mimic the cyclic stresses experienced in real applications. The machining-based fatigue testing approach was demonstrated on welded samples by replicating cyclic stresses of offshore wind turbine monopiles subject to in-service loads. Results The results show that rapid fatigue testing of engineering materials and structures is possible by utilising existing machine tools and centres, which are widely accessible to industry. Cyclic stresses were induced in welded structural steel samples proving the concept of this method. Conclusion This novel fatigue testing method showed that cyclic stresses can be induced by machining cutting forces to address real application needs. The key advantages are that this method can be quickly set up in industry, enabling fast fatigue testing that can lead to reduction of lead times for product and process development of industrial components.

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“…A comprehensive review of ML-based offshore wind farm condition monitoring techniques can be found in [83]. Exploring the landscape of corrosion fatigue assessment in horizontal-axis offshore wind turbines, Okenyia et al advocated the potential of digital twins, amalgamating finite element analysis, material modeling, artificial neural networks, data analytics, and Internet of Things (IoT) with sensor technologies to address challenges in shallow and deep water installations [84]. Additionally, Pezeshki and colleagues conducted an extensive literature review on structural health monitoring for offshore and marine structures, considering the prospect of ML implementations [85].…”

Section: Health Monitoring and Maintenancementioning

confidence: 99%

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Masoumi

2023

JMSE

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The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.

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A review of challenges and framework development for corrosion fatigue life assessment of monopile-supported horizontal-axis offshore wind turbines

Cited by 12 publications

References 115 publications

Advancing digital healthcare engineering for aging ships and offshore structures: an in-depth review and feasibility analysis

Advancing digital healthcare engineering for aging ships and offshore structures: an in-depth review and feasibility analysis

Fatigue Testing Approach Utilising Machining Cutting Forces and Fixture Design

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

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