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首页> 外文期刊>Composite Structures >Experimental validation of vibration-based damage detection for static laminated composite shells partially filled with fluid
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Experimental validation of vibration-based damage detection for static laminated composite shells partially filled with fluid

机译:部分填充流体的静态层压复合材料壳基于振动的损伤检测的实验验证

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摘要

A vibration-based damage detection method for a static laminated composite shell partially filled with fluid (LCSFF) is presented and validated by experiment. The crack damage is simulated using advanced composite damage mechanics in a dynamic finite element model, in which the interaction between the fluid and the composite shell is considered. The accuracy of FE model is first validated by comparing the computed and measured structural frequency response function. Structural damage indexes are constructed and calculated based on energy variation of the structural vibration responses decomposed using wavelet package before and after the occurrence of structural damage. An artificial neural network (ANN) is trained using numerically simulated structural damage index to establish the mapping relationship between the structural damage index and damage status. The test specimen used in experiment contains a cut in its surface made by laser cutting system. Response signals of both intact and damaged specimen are measured and used to construct the corresponding damage indices. The damage status is successfully identified using ANN, indicating that the method adopted in this paper can be applied to online structural damage detection and health monitoring for static LCSFF.
机译:提出了一种基于振动的部分填充液体的静态层压复合材料壳体的损伤检测方法(LCSFF),并通过实验进行了验证。在动态有限元模型中,使用先进的复合损伤机制模拟了裂纹损伤,其中考虑了流体与复合壳之间的相互作用。首先通过比较计算和测量的结构频率响应函数来验证有限元模型的准确性。基于损伤发生前后的小波包分解的结构振动响应的能量变化,构造和计算结构损伤指数。使用数值模拟的结构破坏指数来训练人工神经网络(ANN),以建立结构破坏指数与破坏状态之间的映射关系。实验中使用的试样在其表面上有一个由激光切割系统制成的切口。测量完整样本和受损样本的响应信号,并将其用于构建相应的破坏指数。利用人工神经网络成功识别了损伤状态,表明本文所采用的方法可以应用于静态LCSFF的在线结构损伤检测和健康监测。

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