A Synthetic Reliability-Aware PINN Benchmark for Offshore Wind Turbine Support-Structure Monitoring with Bayesian Inverse Identification
arXiv:2606.24176v1 Announce Type: new Abstract: Reliable structural health monitoring (SHM) of offshore wind turbine (OWT) support structures requires fast state estimation from sparse measurements. Repeated high fidelity finite element or aeroelastic analyses are difficult to use directly in online monitoring loops, while purely data-driven surrogates can require large training sets. This paper presents Digi Turbine, a synthetic reliability-aware Physics Informed Neural Network (PINN) benchmark for OWT monopile support structure monitoring. The workflow embeds a simplified Euler Bernoulli bea
This publication leverages recent advancements in Physics-Informed Neural Networks (PINNs) and Bayesian inference to address a critical need for efficient monitoring of critical infrastructure like offshore wind turbines.
Improving the reliability and efficiency of structural health monitoring for offshore wind turbines is crucial for enhancing renewable energy infrastructure and reducing operational costs and risks.
The development of reliable, data-efficient monitoring benchmarks like Digi Turbine could accelerate the adoption of advanced AI for infrastructure lifespan management and predictive maintenance.
- · Renewable energy sector
- · AI/ML solution providers
- · Offshore wind farm operators
- · Infrastructure maintenance companies
- · Companies reliant solely on traditional, labor-intensive inspection methods
More robust and cost-effective monitoring of offshore wind turbine support structures becomes feasible.
The validated application of PINNs in this domain could lead to broader adoption of similar AI techniques for other complex structural monitoring challenges.
Reduced operational expenditures and increased uptime for offshore wind farms, potentially accelerating renewable energy deployment and strengthening national energy security.
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