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Mechanical Systems and Signal Processing
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Mechanical Systems and Signal Processing
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A general substructure-based framework for input-state estimation using limited output measurements

Authors: Tatsis, K. E. (author); Dertimanis, V. K. (author); Papadimitriou, C. (author); Lourens, E. (author); Chatzi, E. N. (author);

A general substructure-based framework for input-state estimation using limited output measurements

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.

Mechanical Systems and Signal Processing, 150

ISSN:0888-3270

ISSN:1096-1216

Countries
Switzerland, Switzerland, Netherlands
Keywords

Structural health monitoring, 310, Bayesian filtering, Input-state estimation, Dynamic substructuring; Reduced-order modeling; Bayesian filtering; Input-state estimation; Response prediction; Structural health monitoring, Response prediction, Dynamic substructuring, Reduced-order modeling

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
33
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18
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