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A general substructure-based framework for input-state estimation using limited output measurements
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DOI：10.1016/j.ymssp.2020.107223.png)
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Abstract
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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 fixedand 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. (C) 2020 The Author(s). Published by Elsevier Ltd.
Keywords:
Dynamic substructuring
Reduced-order modeling
Bayesian filtering
Input-state estimation
Response prediction
Structural health monitoring
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