As the global demand for renewable energy accelerates, offshore wind energy continues to play a central role in the transition to a low-carbon future. However, with current design trends, Offshore Wind Turbines (OWTs) are becoming increasingly susceptible to damage and deterioration over their operational lifetime. These challenges directly affect turbine performance and safety, whilst also driving up maintenance demands and overall energy costs.

To address these issues, Structural Health Monitoring (SHM) has emerged as a powerful and cost-effective solution. By enabling continuous monitoring of system behaviour and the early detection of faults, SHM supports improved reliability, extended service life, and reduced operational risk for offshore wind infrastructure.

Advancing SHM for Offshore Wind Turbines

SLPE recognises both the challenges and the opportunities associated with the evolving offshore wind sector. In response, the company is actively developing advanced SHM tools specifically tailored for OWT applications, combining physics-based modelling with modern data-driven techniques.

At IWSHM-25, SLPE's R&D team showcased recent advancements in this area. One key contribution was a machine-learning-based methodology capable of detecting OWT shutdown events using sensor data. This approach enables automated identification of abnormal operational states, supporting faster diagnostics and informed decision-making.

In addition, the team presented a deterministic framework for estimating the effective soil properties of OWT foundations. Accurate knowledge of foundation-soil interaction is critical for assessing structural performance, fatigue life, and long-term stability, particularly in complex offshore environments.

Embracing Uncertainty with Bayesian Methods

Building on this work, SLPE is now developing a Bayesian framework for foundation property identification. Unlike deterministic approaches, this framework explicitly accounts for uncertainties in measurements, modelling assumptions, and environmental conditions, delivering more robust and reliable parameter estimates.

Alongside this, SLPE is advancing a Bayesian filtering approach for nonlinear systems with unknown inputs. Offshore wind turbines often exhibit nonlinear behaviour and are subjected to highly uncertain and variable loads from wind, waves, and operational effects. This new framework will enable more accurate real-time monitoring and state estimation, further enhancing the effectiveness of SHM strategies for OWTs.

Powered by In-House OWT Modelling Expertise

These developments are underpinned by SLPE's in-house offshore wind turbine models, which are designed to be both highly accurate and flexible. The models reproduce a wide range of OWT processes whilst allowing efficient experimentation, rapid testing of new methodologies, and seamless extensions to address emerging challenges.

Commitment to a Sustainable Offshore Wind Future

Through continuous innovation in SHM, advanced modelling, and uncertainty-aware methodologies, SLPE is contributing to safer, more reliable, and more cost-effective offshore wind energy. These efforts demonstrate the company's strong commitment to advancing sustainable energy technologies and supporting the long-term success of the offshore wind industry.

Conclusion

In the face of increasing structural complexity and operational demands in offshore wind energy, SLPE is positioning itself at the forefront of innovation in Structural Health Monitoring. By integrating physics-based modelling, data-driven intelligence, and uncertainty-aware Bayesian methodologies, SLPE is delivering SHM solutions that are both technically rigorous and practically impactful.

The advancements presented, from machine-learning-driven operational state detection to probabilistic foundation property identification, demonstrate SLPE's ability to address critical challenges across the full lifecycle of offshore wind turbines. Supported by robust in-house OWT modelling capabilities, these developments enable more reliable monitoring, improved decision-making, and enhanced resilience of offshore wind assets.

Through this continued research and development, SLPE reinforces its commitment to enabling safer, longer-lasting, and more cost-efficient offshore wind infrastructure, contributing meaningfully to a sustainable and low-carbon energy future.