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Smart monitoring of aeronautical composites plates based on electromechanical impedance measurements and artificial neural networks

机译:基于机电阻抗测量和人工神经网络的航空复合材料板智能监控

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摘要

This paper presents a structural health monitoring (SHM) method for in situ damage detection and localization in carbon fiber reinforced plates (CFRPs). The detection is achieved using the electromechanical impedance (EMI) technique employing piezoelectric transducers as high-frequency modal sensors.udNumerical simulations based on the finite element method are carried out so as to simulate more than a hundred damage scenarios. Damage metrics are then used to quantify and detect changes between the electromechanical impedance spectrum of a pristine and damaged structure. The localization process relies on artificial neural networks (ANNs) whose inputs are derived from a principal component analysis of the damage metrics. It is shown that the resulting ANN can be used as a tool to predict the in-plane position of a single damage in a laminated composite plate.
机译:本文提出了一种结构健康监测(SHM)方法,用于碳纤维增强板(CFRP)的原位损伤检测和定位。使用机电阻抗(EMI)技术将压电换能器用作高频模态传感器来实现检测。 ud基于有限元方法的数值模拟旨在模拟一百多种损坏情况。损坏度量随后用于量化和检测原始和损坏结构的机电阻抗谱之间的变化。本地化过程依赖于人工神经网络(ANN),其输入来自对损坏指标的主成分分析。结果表明,所得的人工神经网络可以用作预测层压复合板中单个损伤的面内位置的工具。

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