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首页> 外文期刊>Journal of aerospace engineering >KF-Based Multiscale Response Reconstruction under Unknown Inputs with Data Fusion of Multitype Observations
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KF-Based Multiscale Response Reconstruction under Unknown Inputs with Data Fusion of Multitype Observations

机译：基于KF的多尺度响应重建，在未知输入下具有多重性观测的数据融合

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Utilization of multitype measurements including local and global information for structural health monitoring (SHM) has typically outperformed that using solo-type measurements. However, in many practical situations, only partial measurements can be obtained. Therefore, multiscale response reconstruction at the key locations of interest where sensors are not available is required. The Kalman filter (KF) is a powerful tool for optimally estimating the unknown structural states. The classical KF technique is, however, not applicable when the external excitations are unknown. In this paper, a KF-based multiscale response reconstruction under unknown input (MSRR-UI) approach is proposed to circumvent the aforementioned limitations. Based on the principle of KF, an analytical recursive solution of the proposed approach is derived and given. By using a projection matrix, a revised version of the observation equation is obtained. Multitype measurements in a few locations are fused together for response reconstruction. The unknown loading is simultaneously estimated by least-squares estimation (LSE). The effectiveness of the proposed approach is demonstrated via several numerical examples. (c) 2019 American Society of Civil Engineers.
机译：利用包括局部和全局用于结构健康监测（SHM）的局部和全球信息的测量通常表现优于使用SOLO型测量。然而，在许多实际情况中，只能获得部分测量。因此，需要在不可用的感兴趣的关键位置进行多尺度响应重建。卡尔曼滤波器（KF）是一个强大的工具，用于最佳地估计未知的结构状态。然而，当外部激励未知时，经典的KF技术是不适用的。本文提出了根据未知输入（MSRR-UI）方法的基于KF的多尺度响应重建，以避免上述限制。基于KF的原理，推导出并给出所提出的方法的分析递归解决方案。通过使用投影矩阵，获得观察方程的修订版本。少数位置的多重测量融合在一起进行响应重建。通过最小二乘估计（LSE）同时估计未知的加载。通过几个数值例子证明了所提出的方法的有效性。 （c）2019年美国土木工程学会。

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