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Structural Health Monitoring and Impact Detection Using Neural Networks for Damage Characterization

机译:使用神经网络进行结构健康监测和冲击检测以表征损伤

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摘要

Detection of damage due to foreign object impact is an important factor in the development of new aerospace vehicles. Acoustic waves generated on impact can be detected using a set of piezoelectric transducers, and the location of impact can be determined by triangulation based on the differences in the arrival time of the waves at each of the sensors. These sensors generate electrical signals in response to mechanical motion resulting from the impact as well as from natural vibrations. Due to electrical noise and mechanical vibration, accurately determining these time differentials can be challenging, and even small measurement inaccuracies can lead to significant errors in the computed damage location. Wavelet transforms are used to analyze the signals at multiple levels of detail, allowing the signals resulting from the impact to be isolated from ambient electromechanical noise. Data extracted from these transformed signals are input to an artificial neural network to aid in identifying the moment of impact from the transformed signals. By distinguishing which of the signal components are resultant from the impact and which are characteristic of noise and normal aerodynamic loads, the time differentials as well as the location of damage can be accurately assessed. The combination of wavelet transformations and neural network processing results in an efficient and accurate approach for passive in-flight detection of foreign object damage.
机译:检测由于异物撞击造成的损坏是开发新型航空航天器的重要因素。可以使用一组压电换能器检测在撞击时产生的声波,并且可以基于波在每个传感器到达时间的差异,通过三角测量确定撞击的位置。这些传感器响应于冲击以及自然振动产生的机械运动产生电信号。由于电气噪声和机械振动,准确确定这些时间差可能会带来挑战,甚至很小的测量误差也可能导致计算出的损坏位置出现重大误差。小波变换用于分析多个细节级别的信号,从而将撞击产生的信号与周围的机电噪声隔离开来。从这些变换信号中提取的数据输入到人工神经网络,以帮助识别来自变换信号的冲击时刻。通过区分哪些信号分量是撞击产生的,哪些是噪声和正常空气动力学负载的特征,则可以精确地评估时间差以及损坏的位置。小波变换和神经网络处理相结合,可提供一种有效且准确的被动飞行中异物损坏检测方法。

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    Ross, Richard W.;

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  • 年度 2006
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