Impact load identification for composite structures using Bayesian regularization and unscented Kalman filter
Impact load identification for composite structures using Bayesian regularization and unscented Kalman filter
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DOI：
10.1002/stc.1910
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发表时间：
2017-05
影响因子：
5.4
通讯作者：
G. Yan;Hao Sun;O. Büyüköztürk
中科院分区：
文献类型：
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作者：
G. Yan;Hao Sun;O. Büyüköztürk
在复合材料结构健康监测中，一项重要的任务就是检测和识别可能引起隐形内部损伤的低速冲击事件。本文提出了一种新的方法，利用传感器网络记录的动态测量，同时识别撞击位置并重建作用在复合材料结构上的撞击力时程。该方法由两部分组成：(1)用于重建撞击力时间历程的内环；(2)用于寻找碰撞位置的外环。在内环中，采用了一种新的贝叶斯正则化反分析方法来解决基于状态空间模型的不适定冲击力重构问题。在外环中，采用非线性无迹卡尔曼滤波(UKF)方法，通过最小化测量值和预测响应之间的误差来递归估计撞击位置。以复合材料板为例，说明了新提出的冲击载荷识别方法。实验结果证明了该方法在冲击载荷识别中的有效性和适用性。版权所有©2016 John Wiley&Sons，Ltd.
In structural health monitoring of composite structures, one important task is to detect and identify the low‐velocity impact events, which may cause invisible internal damages. This paper presents a novel approach for simultaneously identifying the impact location and reconstructing the impact force time history acting on a composite structure using dynamic measurements recorded by a sensor network. The proposed approach consists of two parts: (1) an inner loop to reconstruct the impact force time history and (2) an outer loop to search for the impact location. In the inner loop, a newly developed inverse analysis method with Bayesian inference regularization is employed to solve the ill‐posed impact force reconstruction problem using a state‐space model. In the outer loop, a nonlinear unscented Kalman filter (UKF) method is used to recursively estimate the impact location by minimizing the error between the measurements and the predicted responses. The newly proposed impact load identification approach is illustrated by numerical examples performed on a composite plate. Results have demonstrated the effectiveness and applicability of the proposed approach to impact load identification. Copyright © 2016 John Wiley & Sons, Ltd.