基于随机功能池模型的复合材料无人机机翼损伤定位与震级估计

Peiyuan Zhou, Otis Kopsaftopoulos
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摘要

提出了一种基于振动主动感知的全局SHM方法,并对其在复杂机翼结构上的损伤定位和量化精度进行了评价。在此过程中,机翼结构由白噪声振动驱动,响应信号由分布式传感器网络采集。所提出的SHM方法首先利用自回归外生(ARX)模型[1]来表示不同损伤条件下每个传感器位置的时域响应,将结构响应中的随机性最小化并识别出来。然后通过矢量依赖的功能池(vector-dependent functionally pooled, VFP)方法将ARX模型映射到损伤参数空间[2]。然后,提出了一种基于最小化VFP-ARX模型预测误差的损伤估计算法。最后,对损伤估计结果进行了评价,考虑了在SHM过程中利用多传感器信号的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DAMAGE LOCALIZATION AND MAGNITUDE ESTIMATION ON A COMPOSITE UAV WING VIA STOCHASTIC FUNCTIONALLY POOLED MODELS
A vibration-based active-sensing global SHM method is proposed and evaluated for its damage localization and quantification accuracy on complex wing structure. In the process, the wing structure is actuated by a white noise vibration and the response signals are collected by a distributed sensor network. The proposed SHM method first utilize auto-regressive exogenous (ARX) model [1] for representing the time-domain response at each sensor location under various damage conditions, where stochasticity contained in structural response is minimized and identified. ARX models are then mapped to damage parameter space via vector-dependent functionally pooled (VFP) method [2]. Then, a damage estimation algorithm based on minimizing VFP-ARX model prediction error is developed. Finally, the damage estimation results are evaluated as the possibility of leveraging multiple senor signal in SHM process is implicated.
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