Risk-based maintenance strategy selection for wind turbine composite blades

Lopez, Javier Contreras; Kolios, Athanasios

doi:10.1016/j.egyr.2022.04.027

Cited by 27 publications

(7 citation statements)

References 53 publications

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“…This comprehensive framework enables systematic analysis of each risk category, identifying critical risk factors, causes, and potential effects to inform mitigation strategies. Its outcomes enhance the safety and reliability of drone operations in wind turbine inspections while serving as a valuable model for evaluating risks in emerging technologies and industries [15].…”

Section: Risk Classificationmentioning

confidence: 99%

Assessing risks for the use of drones for wind turbine inspections

Kolios

2024

J. Phys.: Conf. Ser.

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This paper presents a comprehensive risk assessment framework for integrating drones into wind turbine inspections, addressing technical, operational, human factors, environmental, and regulatory risks. Utilizing a multidimensional approach based on Failure Modes Effects and Criticality Analysis (FMECA), the study evaluates potential risks in drone-assisted wind turbine inspections, considering various factors like communication reliability, sensor functionality, battery performance, and environmental impacts. The research emphasizes the importance of systematic risk identification, evaluation, and prioritization in enhancing the safety and reliability of drone operations in the renewable energy sector. The framework’s practical application is demonstrated through a case study involving real-world offshore wind farms, engaging industry experts and employing structured data collection methods. The study identifies, assesses, and offers mitigation strategies for critical risks, underlining the significant potential of drones in optimizing maintenance processes and advancing sustainable energy production. Future research directions include refining risk assessment methodologies, advancing drone technology, and fostering international collaboration for broader adoption of drone-based inspections in renewable energy.

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Section: Risk Classificationmentioning

confidence: 99%

Assessing risks for the use of drones for wind turbine inspections

Kolios

2024

J. Phys.: Conf. Ser.

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“…The FMEA approach is a step-by-step approach for identifying all possible failures in a design, manufacturing, or assembly process, or a product or service. It is particularly useful in evaluating new technologies where historical data may be limited or non-existent [47]. In the context of maritime technology, the FMEA approach offers a systematic process for identifying potential failure modes of a component or system, assessing their severity and determining their effects on the overall system operation.…”

Section: Risk Assessment In Maritime Technologymentioning

confidence: 99%

Retrofitting Technologies for Eco-Friendly Ship Structures: A Risk Analysis Perspective

Kolios

2024

JMSE

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This paper presents a detailed risk assessment framework tailored for retrofitting ship structures towards eco-friendliness. Addressing a critical gap in current research, it proposes a comprehensive strategy integrating technical, environmental, economic, and regulatory considerations. The framework, grounded in the Failure Mode, Effects, and Criticality Analysis (FMECA) approach, adeptly combines quantitative and qualitative methodologies to assess the feasibility and impact of retrofitting technologies. A case study on ferry electrification, highlighting options like fully electric and hybrid propulsion systems, illustrates the application of this framework. Fully Electric Systems pose challenges such as ensuring ample battery capacity and establishing the requisite charging infrastructure, despite offering significant emission reductions. Hybrid systems present a flexible alternative, balancing electric operation with conventional fuel to reduce emissions without compromising range. This study emphasizes a holistic risk mitigation strategy, aligning advanced technological applications with environmental and economic viability within a strict regulatory context. It advocates for specific risk control measures that refine retrofitting practices, guiding the maritime industry towards a more sustainable future within an evolving technological and regulatory landscape.

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“…Corrective maintenance is based on conditions of predictive and preventive maintenance in correlation with the confusion matrix, whose abnormal results indicate possible synthetic failure, or the case in spectra where extraordinary pulses are seen that exceed the permitted threshold of 2.49 a.u. In addition, to identify possible risks that arise at operation time and to correct possible breakdowns or failures, which are presented at the time of wind turbine operation time, an inspection is required to determine the health of wind turbine components, such that the wind turbine can operate optimally [23].…”

Section: Predictive Preventive and Corrective Maintenancementioning

confidence: 99%

“…Predictive maintenance is one of the most important processes to ensure the useful life of a wind turbine, which lasts approximately 20 to 25 years; therefore, securing continuous operation is very important for reducing the time and cost of maintenance. It should be noted that preventive, predictive, or reactive maintenance strategies aim to extend the useful life of wind turbines by performing thorough analysis of wind turbine anomalies to generate more data for maintenance needs [8,[19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions

et al. 2023

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Wind energy is one of the most relevant clean energies today, so wind turbines must have good health and be reliable in operation. Current wind turbines have slender and elastic structures that can be easily damaged through vibrations and compromise their health; therefore, vibration monitoring is essential to ensure safe operation. Here, we present a method for simple wind turbine vibration monitoring in the laboratory by means of an accelerometer placed on a weathervane under different scenarios, with recording of different amplitudes of vibrations caused at a constant speed of 10 km/h. The variables, trends, and data captured during vibration monitoring were then used to implement a prediction system of synthetic failure using machine learning methods such as: Medium Trees, Cubic SVN, Logistic Regression Kernel, Optimized Neural Network, and Bagged Trees, with the last demonstrating an accuracy of up to 0.87%.

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Risk-based maintenance strategy selection for wind turbine composite blades

Cited by 27 publications

References 53 publications

Assessing risks for the use of drones for wind turbine inspections

Assessing risks for the use of drones for wind turbine inspections

Retrofitting Technologies for Eco-Friendly Ship Structures: A Risk Analysis Perspective

A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions

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