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首页> 外文期刊>Journal of aerospace engineering >Identifying Distributed Dynamic Loading in One Spatial Dimension Based on Combing Wavelet Decomposition and Kalman Filter with Unknown Input
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Identifying Distributed Dynamic Loading in One Spatial Dimension Based on Combing Wavelet Decomposition and Kalman Filter with Unknown Input

机译：基于梳理小波分解的一个空间维度识别分布式动态加载，以及具有未知输入的卡尔曼滤波器

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摘要

The identification of distributed dynamic loads is an important but challenging task because a distributed dynamic load is composed of both time and space functions. This paper proposes a novel method for the identification of distributed dynamic loading in one spatial dimension using only partial structural responses. The method is based on combing wavelet decomposition of space functions and the identification of time function by the improved Kalman filter with unknown input (KF-UI) developed by the authors. First, the unknown load space function of a distributed dynamic loading is approximated by wavelet decomposition with unknown scale coefficients. Under the given values of wavelet scale coefficients, the spatial information and the equivalent nodal loads of a beam-type structure can be estimated by finite-element modeling. Structural nodal responses in time-domain and the unknown load time function can be identified based on the improved KF-UI using data fusion of partial measurements of structural acceleration and strain responses. Finally, the objective function is established utilizing the error between the calculated and measured responses, and the optimal wavelet coefficients are estimated by minimizing the objective function. Therefore, the unknown distributed dynamic loading can be estimated by combing the reconstructed load space function from the optimal wavelet coefficients and the identified load time function. Numerical simulations of a simply supported beam under different unknown distributed loading verified the effectiveness of the proposed method.
机译：分布式动态负载的识别是一个重要但具有挑战性的任务，因为分布式动态负载由时间和空间函数组成。本文提出了一种仅使用部分结构响应来识别一种空间尺寸的分布式动态加载的新方法。该方法基于梳理空间函数的小波分解和由作者开发的未知输入（KF-UI）的改进的卡尔曼滤波器识别时间函数。首先，通过具有未知刻度系数的小波分解来近似分布式动态加载的未知负载空间函数。在小波尺度系数的给定值下，可以通过有限元建模估计光束型结构的空间信息和相同的节点载荷。可以基于使用结构加速度和应变响应的部分测量的数据融合来基于改进的KF-UI来识别时域和未知负载时间函数的结构节点响应。最后，利用计算和测量响应之间的误差建立目标函数，通过最小化目标函数来估计最佳小波系数。因此，可以通过从最佳小波系数和所识别的负载时间函数梳理重建的负载空间函数来估计未知的分布式动态加载。不同未知分布式负载下简单支持光束的数值模拟验证了所提出的方法的有效性。

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