A Modal‐Based Kalman Filter Approach and OSP Method for Structural Response Reconstruction

Abstract

The objective of the work is experimental validation and optimal experimental design for structural response reconstruction. A modal-based Kalman filter approach based on excitation identification Kalman filter is proposed for response reconstruction and excitation estimation of structures by using noisy acceleration and strain measurements. Firstly, different filters are introduced and discussed. Secondly, to avoid single type sensors, a displacement reconstruction based on modal method is introduced into the proposed approach. Thirdly, the backward sequential algorithm is given to obtain the optimal sensor locations. It is shown that the proposed method can avoid the divergency in the estimated process of excitation and displacements as a result of incomplete measurements. Reasonable estimates of strains, displacements, velocities, accelerations, and excitations of structures can be accomplished with few acceleration sensors and strain gauges.

Information theoretic-based optimal sensor placement for virtual sensing using augmented Kalman filtering

“…Based on filtering techniques introduced for the case of unknown loads, a number of OSP methods have been developed recently for state estimation and response reconstruction [28,40,41], as well as for load estimation [42,43]. The methods are based on minimizing a scalar measure of the steady-state reconstruction error covariance of the response and/or input with respect to the location of sensors.…”

“…The forward sequential sensor placement (FSSP) technique [49] was used as a heuristic algorithm to carry out the optimization problem in which acceleration and strain sensors were added sequentially at their optimal location that maximizes the estimation error of the reconstructed responses. In a very similar approach, Peng et al [41] proposed the backward sequential placement (BSSP) algorithm [49], instead of the FSSP algorithm, to optimize the location of acceleration and strain sensors.…”

Long‐term validation of virtual sensing of a railway bridge with ballasted superstructure

“…Virtual sensing techniques can be categorized as analytical/model-based or empirical/datadriven [6]. .Model-based methods use a structural dynamics model of the structure, usually a finite element (FE) model, to determine the quantities of interest from the signals of the physical sensors, while data-driven methods require at least a short-term measurement at the location of the virtual sensor [3,7] To the best of our knowledge, there are only two studies for the validation of virtual sensing on railway bridges [7,12]. In the first study, the railway bridge KW51 with a ballast superstructure and a length of 115 m was investigated, and virtual sensing was implemented by using a method to determine the train loads.…”

A modal‐based Kalman filtering framework for mode extraction and decomposition of damped structures

“…In general, Kalman filtering has been mainly applied to estimate the state variable using the measured response and further extended to reconstruct the external load using the inverse problem (e.g., Gillijns & De Moor, 2007;Gordon et al, 1993;Hassanabadi et al, 2022). Peng et al (2019), Hwang et al (2009), Kalman (1960), Kang et al (2012), Lei et al (2019), andNiu et al (2015) proposed a modal-based Kalman filter in conjunction with the optimum sensor placement method for the response reconstruction and excitation estimation of structures by using noisy acceleration and strain measurements. Recently, a Kalman filter-based subspace identification has been developed to identify the structural parameters while rejecting the influence of the periodic input rendering the modal parameter estimation difficult (Gres et al, 2021).…”