Due to their unpredictable nature, many impact events (e.g., overheight vehicles striking on bridges) go unnoticed or get reported many hours later. However, they can induce structural failures or hidden damage that accelerates the structure’s long-term degradation. Therefore, prompt impact detection and localization strategies are essential for early warning of impact events and rapid maintenance of structures. Most existing impact detection strategies are developed for aircraft composite panels utilizing high-rate synchronized measurement from densely deployed sensors. Limited efforts have been made for infrastructure or human habitats which generally require large-scale but low-rate measurement. In particular, due to harsh environments (e.g., deep space habitats under meteoroids), structural impact localization must be robust to limited sensors (e.g., sensor damage during impacts) and multi-source errors (e.g., measurement errors). In this study, an effective impact detection and localization strategy is proposed using a limited number of vibration measurements, especially in harsh environments (e.g. in deep space). Convolutional neural networks are trained for each sensor node and are fused using Bayesian theory to improve the accuracy of impact localization. Special considerations are paid to evaluate the effect of both measurement error and modeling error in the analysis. The proposed strategy is illustrated using 1D structure, and further validated in 3D geodesic dome structure numerically. The results demonstrate that it can detect and localize impact events accurately and robustly on structures.

中文翻译：

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使用卷积神经网络和贝叶斯信息融合与有限传感器进行有效的结构影响检测和定位


由于其不可预测性，许多撞击事件（例如，超高车辆撞击桥梁）被忽视或在许多小时后才被报告。然而，它们会诱发结构故障或隐藏的损坏，从而加速结构的长期退化。因此，及时的撞击检测和定位策略对于撞击事件的早期预警和结构的快速维护至关重要。大多数现有的撞击检测策略都是为飞机复合板开发的，利用密集部署的传感器的高速同步测量。对于通常需要大规模但低速率测量的基础设施或人类栖息地所做的努力有限。特别是，由于环境恶劣（例如，流星体下的深空栖息地），结构撞击定位必须对有限的传感器（例如，撞击过程中的传感器损坏）和多源误差（例如，测量误差）具有鲁棒性。在本研究中，提出了一种有效的冲击检测和定位策略，使用有限数量的振动测量，尤其是在恶劣环境（例如在深空）中。针对每个传感器节点训练卷积神经网络，并使用贝叶斯理论进行融合，以提高撞击定位的准确性。在评估分析中测量误差和建模误差的影响时，需要特别考虑。使用一维结构说明了所提出的策略，并在三维测地线圆顶结构中进行了数值验证。结果表明，它可以准确、稳健地检测和定位结构上的撞击事件。