Abstract 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.

中文翻译：

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一种基于移动加权最小二乘法的负荷识别时域方法

摘要 基于格林核函数法(GKFM)的思想，提出了一种改进的移动加权最小二乘法(MWLST)时域载荷识别方法，该方法能准确拟合动载荷。该方法优于使用移动最小二乘拟合（SFM_MLSF）的传统形状函数方法，该方法考虑了两个相邻采样点之间动态载荷的连续性和相关性，并涉及采样点对拟合点的加权贡献。在数值例子中，该方法利用高斯、三次和四次样条权重函数来实现核矩阵的重构。发现当采用它们的最佳支持域半径时，载荷识别的精度几乎相同。此外，数值结果表明，与GKFM和SFM_MLSF相同的不适定正则化方法相比，该方法能更准确、更平滑地识别动态载荷，并且该方法具有良好的稳定性和鲁棒性。此外，提出了一种整体识别和截断处理识别相结合的特殊技术来识别吊装过程中的外部动态载荷，解决了直接使用反演方法引起的振荡问题。