A general substructure-based framework for input-state estimation using limited output measurements. (March 2021)

• Record Type: Journal Article
• Authors: Tatsis, K.E.; Dertimanis, V.K.; Papadimitriou, C.; Lourens, E.; Chatzi, E.N.
• Is Part Of: Mechanical systems and signal processing. Volume 150(2021)
• Publication Date: 2021-03
• Subjects: Dynamic substructuring -- Reduced-order modeling -- Bayesian filtering -- Input-state estimation -- Response prediction -- Structural health monitoring; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.107223
• Languages: English
• ISSNs: 0888-3270

Abstract

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.