Digital technologies can be used to gather accurate information about the behavior of structural components for improving systems design, as well as for enabling advanced Structural Health Monitoring strategies. New avenues for achieving automated and continuous structural assessment are opened up via development of virtualization approaches delivering so-called Digital Twins, i.e., digital mirrored representations of physical. In this framework, the main motivation of this work stems from the existing challenges in the implementation and deployment of a real-time predictive framework for virtualization of dynamic systems. Kalman-based filters are usually employed in this context to address the task of joint input-state prediction in structural dynamics. A Gaussian Process Latent Force Model (GPLFM) approach is exploited in this work to construct flexible data-driven a priori models for the unknown inputs, which are then coupled with a mechanistic model of the structural component under study for input-state estimation. The use of GP regression for this task overcomes the limitations of the conventional random-walk model, thus limiting the necessity of offline user-dependent calibration of this type of data assimilation methods. This paper proposes the use of alternative covariance functions for GP regression in structural dynamics. A theoretical analysis of the GPLFMs linked to the investigated covariance functions is offered. The outcome of this study provides insights into the applicability of each covariance type for GP-based input-state estimation. The proposed framework is validated via an illustrative simulated example, namely a 3 Degrees of Freedom system subjected to an array of different loading scenarios. Additionally, the performance of the method is experimentally assessed on the task of joint input-state estimation during testing of a 3D-printed scaled wind turbine blade.

翻译：数字技术可用于收集结构部件行为的精确信息，以改进系统设计并实现先进的健康监测策略。通过开发提供所谓数字孪生（即物理实体的数字镜像表示）的虚拟化方法，为自动化、连续结构评估开辟了新途径。在此框架下，本研究的主要动机源于在动态系统虚拟化的实时预测框架实施与部署中存在的现有挑战。通常，卡尔曼类滤波器被用于处理结构动力学中的联合输入-状态预测任务。本文利用高斯过程潜力量模型（GPLFM）方法构建灵活的数据驱动先验模型，用于未知输入，随后将其与所研究结构部件的机理模型耦合以实现输入-状态估计。采用高斯过程回归解决此任务克服了传统随机游走模型的局限性，从而减少了此类数据同化方法中离线依赖用户标定的必要性。本文提出在结构动力学中使用替代协方差函数进行高斯过程回归。针对所研究的协方差函数，提供了与之关联的GPLFM理论分析。本研究结果揭示了每种协方差类型在高斯过程输入-状态估计中的适用性。通过一个说明性仿真示例（即承受多种荷载场景的三自由度系统）验证了所提框架。此外，在3D打印缩比风力涡轮机叶片的测试中，通过联合输入-状态估计任务实验评估了该方法性能。