Offshore wind turbines are complex systems operating in harsh environment. The dynamic demands in these systems often differ from values used in design, leading to unexpected mechanical and structural failures. This signifies the importance of remote monitoring technologies for damage diagnosis and prognosis in offshore wind turbines. This study is focused on developing mechanics‐based digital twins for offshore wind turbine monitoring through a model‐updating process using sparse measurement data. Digital twins can be used to estimate the system unmeasured response (i.e., virtual sensing) and to predict the remaining useful fatigue life and failure point of different structural components. A time‐domain sequential Bayesian finite element model updating is proposed for mechanics‐based digital twinning. This approach is formulated for application to offshore wind turbine and jointly estimates the updating model parameters and the time history of unknown input forces. A classical modal‐based model updating followed by modal expansion method is also implemented for comparison. In this approach, updating model parameters are estimated to minimize the discrepancies between the identified and model‐predicted modal parameters of the turbine. The performance of these two approaches are studied on a 2‐MW offshore wind turbine at the Blyth wind farm in the United Kingdom. Strain response time history at mudline is estimated through both approaches and compared with actual measurements for validation. It is observed that both approaches are capable of accurate response prediction while the Bayesian approach leads to slightly better results. Furthermore, the Bayesian approach allows for identification of input loads and uncertainty quantification.

中文翻译：

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基于机械模型的稀疏测量更新，用于识别和虚拟感测海上风力涡轮机

海上风力涡轮机是在恶劣环境下运行的复杂系统。这些系统的动态需求通常与设计中使用的值不同，从而导致意外的机械和结构故障。这标志着远程监控技术对于海上风力发电机组的损伤诊断和预测的重要性。这项研究的重点是通过使用稀疏测量数据的模型更新过程来开发用于海上风力涡轮机监控的基于力学的数字双胞胎。数字孪生可用于估计系统未测到的响应（即虚拟感测），并预测不同结构组件的剩余可用疲劳寿命和失效点。针对基于力学的数字孪生，提出了时域顺序贝叶斯有限元模型更新。该方法适用于海上风力发电机，可共同估算更新的模型参数和未知输入力的时间历程。还进行了经典的基于模态的模型更新和模态扩展方法的比较。在这种方法中，估计模型参数的更新可最大程度地减少已识别出的和模型预测出的涡轮模态参数之间的差异。在英国的布莱斯风电场，使用2兆瓦的海上风力涡轮机研究了这两种方法的性能。通过两种方法都可以估算泥线处的应变响应时间历史，并将其与实际测量值进行比较以进行验证。可以观察到，两种方法都能够进行准确的响应预测，而贝叶斯方法则可以得到更好的结果。此外，