Study suggests reliability-based inspection can help manage fatigue-driven risks
Some support structures for offshore wind turbines could fall short of required fatigue life expectations, according to a new report from classification society Lloyd’s Register (LR).
As the classification society notes, predictive maintenance is commonly used for offshore structures and can be applied to offshore wind turbines, but turbines and the structures supporting them face intense wind-induced fatigue, and predicting fatigue remains challenging.
Fatigue can lead to failures in key components such as blades, towers, and foundations. Fatigue analysis considers gravity, aerodynamic and hydrodynamic forces, and loads from wind and wave to estimate cumulative damage but, as LR points out, wind turbine support structures are mostly fatigue driven, compared with oil and gas structures, because of significant wind-induced fatigue loading.
Predictive maintenance using fatigue reliability models gives operators of offshore wind turbines a risk-based inspection (RBI) approach to focus efforts where they matter most. “While fatigue-based inspection planning is effective, it requires expert input, reliable models and software tools that can handle complex calculations. The right inspection method is critical to balance safety against cost,” LR says.
The LR study, Fatigue Reliability of Offshore Wind Turbine Structures: A case study, evaluated a North Atlantic offshore windfarm with a capacity of 500-600 MW capacity and 60–70 turbines. As LR notes, offshore wind turbines are typically designed for 25 years of service, using a fatigue design factor of three – implying a minimum required fatigue life of 75 years. Overestimating fatigue life may lead to unnecessary costs, while underestimation risks safety. Therefore, the accuracy of analysis methodology, load modelling, material properties – such as stress-life curves and fatigue limits – is critical for the fatigue assessment being the major driving aspect for the structural design optimisation for long-term integrity of the asset. In fact, LR’s study found that a critical joint in a jacket foundation would reach the end of its fatigue life after just 52 years, falling short of this design requirement.
Instead of redesigning the joint, the study took an RBI approach to identify and mitigate potential failure through targeted, risk-based maintenance. The study combined a stress versus number of cycles model to estimate when structural safety drops below acceptable thresholds, with fracture mechanics crack growth analysis, to predict the probability of failure over time and inform inspection intervals.
This approach incorporates inspection results via probability of detection (PoD) curves to allow inspection schedules to be dynamically updated, responding to real-world conditions and inspection findings.
The results suggest the first inspection should be carried out at around year nine after installation. After that, depending on the inspection method, further inspections might be needed every year to maintain acceptable safety margins.
However, the case study highlights the limitations of current inspection methods. Visual and ultrasonic inspections were found to be less effective for fatigue-critical components. More advanced techniques, such as eddy current or alternating current field measurement, offer greater reliability and allow for longer inspection intervals, but only when operators were willing to adopt slightly lower safety thresholds.
While RBI planning is effective in reducing in-service life costs and ensuring the longevity and safety for structures for offshore wind turbines, it requires expert input, reliable models and software tools that can handle complex calculations.
LR says ongoing research aims to refine the models and address the challenges during their application. Reliability updating, especially when integrating PoD curves, requires complex modelling and precise calibration of parameters such as initial crack size and stress intensity factors, areas often underdeveloped in practice.
LR global head of technology offshore and subsea Kourosh Parsa says, “Many offshore wind assets are designed to a standard fatigue factor, but real-world conditions often expose critical vulnerabilities.
“Our findings show that using reliability-based methods allows operators to focus inspections where the risks are greatest. By integrating sophisticated models and real-world inspection data, we can extend asset life, reduce costs and, most importantly, maintain safety.”
LR head of offshore renewable solutions Manuel Ruiz says, “By focusing on the areas with the greatest risk, we not only help to manage fatigue-related issues more effectively, we are also enabling developers and operators to make better-informed decisions that optimise asset life and performance.
“This proactive, risk-based approach is exactly how we support our clients in navigating complexity, controlling costs and ensuring the long-term viability of their offshore wind investments.”
“The fatigue reliability-based inspection strategy is a powerful tool to minimise in-service life costs while ensuring safety for the offshore wind turbines structures,” LR concludes.
“The fatigue reliability analysis for developing an inspection schedule is an integrated approach which considers sophisticated and advanced fatigue analysis methods, fatigue cracking, reliability, and inspection of offshore structures. This approach aids professionals involved in design, structural appraisal, and subsea inspection of steel jacket structures.
“This requires extensive effort and expertise in understanding and mastering various techniques and tools required to perform the assessment. Although the standards and various literature available discuss the methodology and the procedure, there are areas which need more research and information.”
Having undertaken the study, LR has called for wider industry collaboration to refine inspection standards, share real-time monitoring data to refine fatigue predictions, and adopt more flexible definitions of acceptable reliability where appropriate.
It recommends in-service loading data based on real-time monitoring should be effectively utilised for the updating of reliability (as reduced equivalent stresses or factors on SCFs). It also recommends the target reliability should be determined appropriately considering the accuracy of fatigue calculation models and the in-service loading data.
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