A time-domain method for load identification using moving weighted least square technique

• Published in: Computers & structures Vol. 234; p. 106254
• Main Authors: Sun, Yuantao, Luo, Lifu, Chen, Kaige, Qin, Xianrong, Zhang, Qing
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.07.2020; Elsevier BV
• Subjects: Dynamic loads; Green’s kernel function; Identification; Kernel functions; Least squares; Load; Load identification; Mathematical analysis; Moving weighted least square; Reconstruction of kernel matrix; Regularization; Regularization method; Regularization methods; Robustness (mathematics); Sampling; Shape functions; Time domain analysis; Weighting functions; Load identification; Reconstruction of kernel matrix; Regularization method; Moving weighted least square; Green’s kernel function
• ISSN: 0045-7949; 1879-2243
• DOI: 10.1016/j.compstruc.2020.106254

•A new time-domain method by reconstructing kernel matrix for load identification.•Moving weighted least square was utilized to construct load fitting function.•Shape function loads under Gauss, cubic spline and quartic spline weight functions.•Optimum supported domain radii of three weight functions for load identification.•A special technique to solve the ill-posed problem in identifying hoisting loads. Based on the thought of Green’s kernel function method (GKFM), an improved time-domain load identification method using moving weighted least square technique (MWLST) which can accurately fit dynamic load is proposed. Better than the traditional shape function method using moving least square fitting (SFM_MLSF), the proposed method considers continuity and correlation of dynamic load between two adjacent sampling points, and involves the weighted contribution of sampling points to the fitting point. In numerical examples, Gauss, Cubic and Quartic spline weight functions are utilized in the proposed method to realize the reconstruction of kernel matrix. It is found that the accuracies of load identification are almost same when their optimum supported domain radii are adopted. Furthermore, the numerical results illustrate that the proposed method can identify dynamic load more accurately and smoothly than GKFM and SFM_MLSF significantly by the same regularization method for ill-posedness, and the proposed method has excellent stability and robustness. Additionally, a special technique combining both the whole identification and the truncated-processing identification is proposed to identify external dynamic loads during hoisting process, which solves the oscillation problem caused by using inversing methods directly.