Load identification with regularized total least-squares method

Abstract

Load identification in structural dynamics is an ill-conditioned inverse problem, and the errors existing in both the frequency response function matrix and the acceleration response have a great influence on the accuracy of identification. The Tikhonov regularized least-squares method, which is a common approach for load identification, takes the effect of the acceleration response errors into account but neglects the effect of the errors of the frequency response function matrix. In this article, a Tikhonov regularized total least-squares method for load identification is presented. First, the total least-squares method which can minimize the errors of the frequency response function matrix and acceleration response simultaneously is introduced into load identification. Then Tikhonov regularization is used to regularize the total least-squares method to improve the ill-conditioning of the frequency response function matrix. The regularization parameter is selected by the L-curve criterion. To validate the performance of the regularized total least-squares method, a load identification simulation with two excitation loads is studied on a plate based on the finite element method and a load identification experiment with two excitation loads is conducted on an aluminum plate. Both simulation and experiment results show that the excitation loads identified by the regularized total least-squares method match the actual loads well although there are errors existing in both the frequency response function matrix and acceleration response. In experiment, the average relative error of the regularized total least-squares method is 13.00% for excitation load 1 and 20.02% for excitation load 2, whereas the average relative error of the regularized least-squares method is 35.86% and 53.09% for excitation load 1 and excitation load 2, respectively. This result reveals that the regularized total least-squares method is more effective than the regularized least-squares method for load identification.

The Application of Piecewise Regularization Reconstruction to the Calibration of Strain Beams

“…Miao et al [21] proposed a finite element modification model combined with the Tikhonov regularization method for the reconstruction of a periodic load. Tang et al [22] proposed a Tikhonov regularized total least squares method and verified the load reconstruction on an aluminum plate. Sun et al [23] combined matrix equations and regularization methods to derive unbalanced forces based on vibration responses.…”

The determination of the regularization parameter based on signal-to-noise ratio in load identification

“…and the structural characteristics (Choi et al, 2006). To identify different types of loads, various load identification methods and algorithms have been proposed, such as least-squares (Sun et al, 2020), total least-squares (Jia et al, 2015; Tang et al, 2021), B-spline method (Gunawan et al, 2006), etc. These methods can be divided into two main classes: frequency domain method (Rezayat et al, 2016) and time domain method (Draper Iii and Lee, 2019; Kulkarni et al, 2020).…”