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首页> 外文会议>Society for Machinery Failure Prevention Technology Meeting; 20050418-21; Virginia Beach,VA(US) >AUTONOMOUS IMPACT DAMAGE DETECTION AND ISOLATION PREDICTION FOR AEROSPACE STRUCTURES
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AUTONOMOUS IMPACT DAMAGE DETECTION AND ISOLATION PREDICTION FOR AEROSPACE STRUCTURES

机译：航空结构的自主碰撞损伤检测和隔离预测

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摘要

This paper presents a practical yet innovative impact damage identification and prognosis approach for aerospace structures that uses an optimized suite of reliable COTS sensors coupled with advanced damage detection and modeling algorithms. The presented methodology utilizes a monitoring approach based on acceleration measurements that are analyzed using advanced signal processing and dispersive wave theory models that capture frequency and orientation dependent wave propagation effects. The acceleration measurements and associated processing modules are used to provide immediate detection and isolation estimates, while an energy amplitude feature allows for assessments of damage severity after the impact. By embedding wave theory model results with the adaptive signal processing algorithms, a more accurate understanding of the time-frequency behavior of the dispersive waves produced at impact is gained. Damage localization is performed based on the comparison between the predicted and measured wave group velocities, with a genetic algorithm used to optimize the parameters of a triangulation procedure. This combination of model and feature-based algorithms allows the system to make use of limited, but readily available accelerometer data. This procedure also minimizes the learning and modeling difficulties associated with other techniques that are based solely on models or measurements. A few selected demonstrations are presented that illustrate the impact location prediction capabilities in realistic carbon fiber reinforced polymer (CFRP) composite panels.
机译：本文提出了一种实用而创新的航空航天结构碰撞损伤识别和预测方法，该方法使用了一套优化的可靠COTS传感器套件以及先进的损伤检测和建模算法。提出的方法利用了基于加速度测量的监视方法，该加速度测量使用高级信号处理和分散波理论模型进行了分析，这些模型捕获了频率和方向相关的波传播效应。加速度测量和相关的处理模块用于提供立即的检测和隔离估计，而能量振幅功能则允许在撞击后评估损伤的严重程度。通过将波理论模型结果与自适应信号处理算法一起嵌入，可以更准确地了解碰撞时产生的色散波的时频行为。基于预测波组速度与测量波组速度之间的比较，使用遗传算法来优化三角剖分程序的参数，从而进行损伤定位。基于模型和基于特征的算法的这种结合使系统可以利用有限但容易获得的加速度计数据。此过程还最大程度地减少了与仅基于模型或测量的其他技术相关的学习和建模困难。展示了一些选定的演示，演示了现实的碳纤维增强聚合物（CFRP）复合面板中的冲击位置预测功能。

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