KF-Based Multiscale Response Reconstruction under Unknown Inputs with Data Fusion of Multitype Observations
Publication: Journal of Aerospace Engineering
Volume 32, Issue 4
Abstract
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.
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Acknowledgments
The authors gratefully acknowledge the financial support from National Natural Science Foundation of China (Grant No. 51708198). The support from Natural Science Foundation of Hunan Province (No. 2018JJ3054) and Fundamental Development Funds for Young Researchers of Hunan University (No. 531107050912) is also greatly appreciated.
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Published In
Journal of Aerospace Engineering
Volume 32 • Issue 4 • July 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Nov 19, 2018
Accepted: Jan 17, 2019
Published online: Apr 12, 2019
Published in print: Jul 1, 2019
Discussion open until: Sep 12, 2019
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