陈玲莉, 田绍华, 陈振茂. 基于振动信号神经网络层合板分层损伤检测研究[J]. 力学与实践, 2011, 33(1): 24-28. DOI: 10.6052/1000-0879-lxysj2010-414
引用本文: 陈玲莉, 田绍华, 陈振茂. 基于振动信号神经网络层合板分层损伤检测研究[J]. 力学与实践, 2011, 33(1): 24-28. DOI: 10.6052/1000-0879-lxysj2010-414
VIBRATION-BASED DELAMINATION IDENTIFICATION FOR COMPOSITES LAMINATES BY USING NEURAL NETWORKS[J]. MECHANICS IN ENGINEERING, 2011, 33(1): 24-28. DOI: 10.6052/1000-0879-lxysj2010-414
Citation: VIBRATION-BASED DELAMINATION IDENTIFICATION FOR COMPOSITES LAMINATES BY USING NEURAL NETWORKS[J]. MECHANICS IN ENGINEERING, 2011, 33(1): 24-28. DOI: 10.6052/1000-0879-lxysj2010-414

基于振动信号神经网络层合板分层损伤检测研究

VIBRATION-BASED DELAMINATION IDENTIFICATION FOR COMPOSITES LAMINATES BY USING NEURAL NETWORKS

  • 摘要: 基于振动信号应用神经网络研究层合板分层损伤的检测方法. 对层合板分层损伤区域, 采用相同坐标不同节点建立了分层损伤处的有限元模型; 通过数值模拟提取结构无损和不同程度面积分层损伤的全局振动标识量; 重点研究神经网络对层合板分层损伤位置和损伤程度的检测技术. 研究表明, 用结构全局振动标识量作为人工神经网络的输入, 对层合板结构分层损伤检测是一种很有效的工程实用技术, 可应用于实际结构的在线损伤检测.

     

    Abstract: In this paper, a delamination identification strategybased on vibration signatures by using artificial neural networks ispresented Through different nodes with same coordinates, a finite elementmodel for internal delamination is established.The global vibrationidentification factors of damaged and damage free laminates are obtained bynumerical simulation. In the studies, the identification capability for thequantitative prediction of delamination in composites laminates is focused.The results show that the strategy based on the vibration signal measurementand artificial neural networks is efficient to detect delamination defectsand there is a good possiblity to apply the proposed method to the helthmonitoring of a practical composite structure.

     

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