通过高斯过程潜在力模型虚拟感知单桩基础海上风电涡轮机亚土壤应变响应
Virtual sensing of subsoil strain response in monopile-based offshore wind turbines via Gaussian process latent force models
AI总结：
本文提出利用高斯过程潜在力模型实现单桩基础海上风电涡轮机亚土壤应变的虚拟感知，通过结合物理模型与数据驱动模型，验证该方法在实际场景中的有效性。
摘要：
虚拟感知技术在单桩基础海上风电涡轮机结构健康监测中日益受到关注，因为泥线以下的应变响应是疲劳损伤累积的主要指标，难以用物理仪器直接测量。高斯过程潜在力模型（GPFLM）是一种通用的贝叶斯虚拟感知技术，结合了结构的物理模型和系统的潜在变量数据驱动模型，以推断未测量的应变状态。在GPFLM中，将未知的激励源建模为高斯过程（GP）以促进应变估计，通过GP协方差核的性质以及机械模型提供的相关性信息，提供完整的随机特征描述。通过高斯过程回归测量的加速度，利用扩展状态空间模型中的卡尔曼滤波和Rauch-Tung-Striebel平滑高效计算后验推断。虽然GPFLM在数值研究中已被证明在准确性、鲁棒性和数值稳定性方面优于其他虚拟感知技术，但本文提供了首次现场验证GPFLM的案例。通过比较GPFLM预测的应变响应与从荷兰Westermeerwind Park运营海上风电涡轮机收集的亚土壤应变数据。
英文摘要：
Virtual sensing techniques have gained traction in applications to the structural health monitoring of monopile-based offshore wind turbines, as the strain response below the mudline, which is a primary indicator of fatigue damage accumulation, is impractical to measure directly with physical instrumentation. The Gaussian process latent force model (GPFLM) is a generalized Bayesian virtual sensing technique which combines a physics-driven model of the structure with a data-driven model of latent variables of the system to extrapolate unmeasured strain states. In the GPLFM, modeling of unknown sources of excitation as a Gaussian process (GP) serves to facilitate strain estimation by providing a complete stochastic characterization of the covariance relationship between input forces and states, using properties of the GP covariance kernel as well as correlation information supplied by the mechanical model. It is shown that posterior inference of the latent inputs and states is performed by Gaussian process regression of measured accelerations, computed efficiently using Kalman filtering and Rauch-Tung-Striebel smoothing in an augmented state-space model. While the GPLFM has been previously demonstrated in numerical studies to improve upon other virtual sensing techniques in terms of accuracy, robustness, and numerical stability, this work provides one of the first cases of in-situ validation of the GPLFM. The predicted strain response by the GPLFM is compared to subsoil strain data collected from an operating offshore wind turbine in the Westermeerwind Park in the Netherlands.
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