Abstract This paper presents a general framework for estimating the state and unknown inputs at the level of a system subdomain using a limited number of output measurements, enabling thus the component-based vibration monitoring or control and providing a novel approach to model updating and hybrid testing applications. Under the premise that the system subdomain dynamics are driven by the unknown (i) externally applied inputs and (ii) interface forces, with the latter representing the unmodeled system components, the problem of output-only response prediction at the substructure level can be tailored to a Bayesian input-state estimation context. As such, the solution is recursively obtained by fusing a Reduced Order Model (ROM) of the structural subdomain of interest with the available response measurements via a Bayesian filter. The proposed framework is without loss of generality established on the basis of fixed- and free-interface domain decomposition methods and verified by means of three simulated Wind Turbine (WT) structure applications of increasing complexity. The performance is assessed in terms of the achieved accuracy on the estimated unknown quantities.

中文翻译：

---

使用有限输出测量进行输入状态估计的基于子结构的通用框架

摘要 本文提出了一个通用框架，用于使用有限数量的输出测量来估计系统子域级别的状态和未知输入，从而实现基于组件的振动监测或控制，并提供一种新的模型更新和混合测试方法。应用程序。在系统子域动力学由未知的 (i) 外部施加的输入和 (ii) 界面力驱动的前提下，后者代表未建模的系统组件，子结构级别的仅输出响应预测问题可以定制到贝叶斯输入状态估计上下文。因此，通过贝叶斯滤波器将感兴趣的结构子域的降阶模型 (ROM) 与可用响应测量值融合，递归地获得解决方案。所提出的框架不失一般性，建立在固定和自由接口域分解方法的基础上，并通过三个日益复杂的模拟风力涡轮机 (WT) 结构应用程序进行验证。性能是根据估计的未知量达到的准确度来评估的。