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.

翻译：虚拟传感技术在单桩式海上风机结构健康监测中的应用日益受到关注，原因是泥面以下应变响应作为疲劳损伤累积的主要指标，难以通过物理仪器直接测量。高斯过程潜在力模型(GPFLM)是一种广义贝叶斯虚拟传感技术，该模型将基于物理驱动的结构模型与系统潜在变量的数据驱动模型相结合，以推演未测量的应变状态。在GPFLM中，将未知激励源建模为高斯过程(GP)，可利用GP协方差核的特性及力学模型提供的相关性信息，通过完整表征输入力与状态之间协方差关系的随机特性，促进应变估计。研究表明，潜在输入与状态的后验推理可通过加速度测量值的高斯过程回归实现，并利用增广状态空间模型中的卡尔曼滤波和Rauch-Tung-Striebel平滑算法高效计算。尽管先前数值研究已证明GPFLM在精度、鲁棒性和数值稳定性方面优于其他虚拟传感技术，但本研究提供了GPFLM的首次原位验证案例。将GPFLM预测的应变响应与荷兰Westermeerwind公园运行中的海上风机地下应变实测数据进行对比分析。