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首页> 外文期刊>Mechanical systems and signal processing >A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure
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A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure

机译：结构健康监测的组合有限元和分层深度学习方法：销联合复合桁架结构试验

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摘要

Structural Health Monitoring (SHM) is an emerging field of engineering with a wide range of applications. The most common SHM strategies operate on structural responses through vibration measurements and focus on training mathematical classifiers which are used after to identify damage in unknown responses. Classifiers may additionally locate damage when adequate labeled damaged data is available. In the present work, a novel SHM method is presented where labeled damaged data is generated through FE models for a pin-joint composite truss structure employing a model-based approach for the problem of data acquisition. The truss is made of carbon fiber reinforced polymer (CFRP) members joint on aluminum connections forming a complex and large FE problem. A Deep Learning (DL) Convolutional Neural Network (CNN) classifier is trained on the FE generated vibration data combined with a hierarchical multiple damage identification and location scheme. The numerically trained CNN is after validated on experimental statuses of the truss in both damage detection and location, proving to be robust and accurate for the considered test case. The potential of hierarchical CNNs with FE based SHM data for multiple damages is investigated in this work and a comparison is given between hierarchical and direct multiclass CNNs. The large performance gains of the former are proven for the studied experimental case highlighting also the importance of SHM system architectures with CNNs.
机译：结构健康监测（SHM）是具有广泛应用的新兴工程领域。最常见的SHM策略通过振动测量和专注于训练数学分类的结构响应，以识别未知反应的损坏。分类器可以在提供足够的标记损坏数据时另外定位损坏。在本作本作中，介绍了一种新的SHM方法，其中通过FE模型产生标记的损坏数据，用于采用基于模型的数据采集问题的针对性复合桁架结构。桁架由碳纤维增强聚合物（CFRP）构件接头组合在铝连接上，形成复杂和大的FE问题。深度学习（DL）卷积神经网络（CNN）分类器训练在FE产生的振动数据上，结合分层多损伤识别和位置方案。在损坏检测和位置的桁架实验状态下验证了数值训练的CNN，证明是考虑的测试用例的鲁棒和准确。在这项工作中研究了对基于Fe的SHM数据的分层CNN的电位，并且在分层和直接多字符CNNS之间给出了比较。前者的大型性能收益对于学习的实验案例已经证明了SHM系统架构与CNNS的重要性。

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