Integrated preventive–proactive–reactive offshore wind farms maintenance planning

Bocewicz, Grzegorz; Frederiksen, Rasmus Dovnborg; Nielsen, Peter; Banaszak, Zbigniew

doi:10.1007/s10479-024-05951-4

Cited by 3 publications

(5 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The primary contribution of this work is a methodology for evaluating specific maintenance operations for Offshore Wind Farms (OWFs) concerning the implementation of predictive maintenance. This continues previous research by the authors, which focused on developing specific reference models for predictive maintenance [11,12], where service mission planning was the focus of the work and not the monetary impact.…”

Section: Introductionsupporting

confidence: 75%

“…The S T service mission includes the allocation of available service teams to selected WTs and their transport using the available fleet of vessels. In the adopted planning process for this type of mission, the following assumptions were made [12]:…”

Section: Service Planning Modelmentioning

confidence: 99%

“…Under predictive maintenance, planning service missions comes down to determining an S T plan whose value of ε ′ guarantees the lowest maintenance costs (6). Declarative programming techniques [12] were used to designate this type of mission. The proposed approach assumes that the considered problem is formulated into a Constraint Optimization Problem (COP).…”

Section: Service Planning Modelmentioning

confidence: 99%

“…Additionally, a prediction score of 0.3 is valuable to prioritize over a prediction score of 0.1, even though both would be classified as having no imminent downtime events. The importance of this was explored in [12], where the prediction scores are used to evaluate the possible impacts of the downtime events.…”

Section: Efficiency Requirementsmentioning

confidence: 99%

“…A membership function for each prediction outcome is then calculated; see Equations ( 9)- (12). These functions will satisfy Requirement 2, as they evaluate each of the four prediction outcomes and then a weighted distance for both the prediction scores, as well as the prediction scores of the opposite event.…”

Section: Prediction Efficiency-continuous Prediction Scoresmentioning

confidence: 99%

See 4 more Smart Citations

Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study

Frederiksen,

Bocewicz,

Radzki

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

The successful implementation of predictive maintenance for offshore wind farms suffers from a poor understanding of the consequential short-term impacts and a lack of research on how to evaluate the cost-efficiency of such efforts. This paper aims to develop a methodology to explore the short-term marginal impacts of predictive maintenance applied to an already existing preventive maintenance strategy. This method will be based on an analysis of the performance of the underlying predictive model and the costs considered under specific maintenance services. To support this analysis, we develop a maintenance efficiency measure able to estimate the efficiency of both the underlying prediction model used for predictive maintenance and the resulting maintenance efficiency. This distinction between the efficiency of the model and the service results will help point out insufficiencies in the predictive maintenance strategy, as well as facilitate calculations on the cost–benefits of the predictive maintenance implementation. This methodology is validated on a realistic case study of an annual service mission for an offshore wind farm and finds that the efficiency metrics described in this paper successfully support cost–benefit estimates.

show abstract

Section: Introductionsupporting

confidence: 75%

Section: Service Planning Modelmentioning

confidence: 99%

Section: Service Planning Modelmentioning

confidence: 99%

Section: Efficiency Requirementsmentioning

confidence: 99%

Section: Prediction Efficiency-continuous Prediction Scoresmentioning

confidence: 99%

See 3 more Smart Citations

Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study

Frederiksen,

Bocewicz,

Radzki

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach

Ali,

El-Thalji,

Giljarhus

et al. 2024

Energies

View full text Add to dashboard Cite

Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have used time-domain and frequency-domain features alongside machine learning techniques to predict faults in wind turbine blades, such as erosion and cracks. However, a key gap remains in integrating these methods into a unified framework for fault prediction, which could offer a more comprehensive solution for diagnosing faults. This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. Open-source vibration data collected from an experimental setup (where a small wind turbine with an artificially eroded and cracked blade was tested) were utilized. The time- and frequency-domain features were extracted and analyzed using various machine learning algorithms. It was found that erosion and crack faults have unique time- and frequency-domain features. The crack fault introduces an amplitude modulation in the vibration time wave, which produces sidebands around the fundamental frequency in the frequency domain. However, erosion fault introduces asymmetricity and flatness to the vibration time wave, which produces harmonics in the frequency-domain plot. The results also highlighted that utilizing both time- and frequency-fault features enhances the performance of the machine learning algorithms. This study further illustrates that even though some machine learning algorithms provide similar high classification accuracy, they might differ in quantifying error Types I, II, and, III, which is extremely important for maintenance engineers, as it might lead to undetected fault events and false alarm events.

show abstract

A Reference Modelling Approach for Cost Optimal Maintenance for Offshore Wind Farms

Frederiksen,

Bocewicz,

Nielsen

et al. 2024

Sustainability

View full text Add to dashboard Cite

This paper presents a novel reference model designed to optimize the integration of preventive and predictive maintenance strategies for offshore wind farms (OWFs), enhancing operational decision-making. The model’s flexible and declarative architecture facilitates the incorporation of new constraints while maintaining computational efficiency, distinguishing it from existing methodologies. Unlike previous research that did not explore the intricate cost dynamics between predictive and preventive maintenance, our approach explicitly addresses the balance between maintenance expenses and wind turbine (WT) downtime costs. We quantify the impacts of these maintenance strategies on key operational metrics, including the Levelized Cost of Energy (LCOE). Using a constraint programming framework, the model enables rapid prototyping of alternative maintenance scenarios, incorporating real-time data on maintenance history, costs, and resource availability. This approach supports the scheduling of service logistics, including the optimization of vessel fleets and service teams. Simulations are used to evaluate the model’s effectiveness in real-world scenarios, such as handling the maintenance of up to 11 wind turbines per business day using no more than four service teams and four vessels, achieving a reduction in overall maintenance costs in simulated case of up to 32% compared to a solution that aims to prevent all downtime events. The prototype implementation as a task-oriented Decision Support System (DSS) further shows its potential in minimizing downtime and optimizing logistics, providing a robust tool for OWF operators.

show abstract

Integrated preventive–proactive–reactive offshore wind farms maintenance planning

Cited by 3 publications

References 59 publications

Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study

Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study

Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach

A Reference Modelling Approach for Cost Optimal Maintenance for Offshore Wind Farms

Contact Info

Product

Resources

About