Constraining Gaussian processes for physics-informed acoustic emission mapping
Constraining Gaussian processes for physics-informed acoustic emission mapping
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DOI：
10.1016/j.ymssp.2022.109984
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Matthew R. Jones;T. Rogers;E. Cross
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文献类型：
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作者：
Matthew R. Jones;T. Rogers;E. Cross
结构损伤的自动定位是实现高价值结构的预测性或基于状态的维护的一个具有挑战性但关键的组成部分。声发射到达时间映射的使用是一种很有前途的方法，这一挑战，但严重阻碍了需要收集一组密集的人工声发射测量的结构，导致在一个漫长的，往往是不切实际的数据采集过程。在本文中，我们考虑使用物理信息高斯过程来学习这些映射以缓解这个问题。在该方法中，高斯过程被约束到物理域，使得与结构的几何形状和边界条件相关的信息直接嵌入到学习过程中，从而返回一个模型，该模型保证所做的任何预测都满足边界处的物理一致性行为。当训练测量获取有限时出现的许多场景，包括训练数据稀疏的情况，以及在感兴趣结构上的有限覆盖范围。使用复杂的板状结构作为实验案例研究，我们表明，我们的方法显着减少了数据收集的负担，可以看出，边界条件知识的结合显着提高了预测精度，因为训练观察减少，特别是当训练测量不是在结构的所有部分。
The automated localisation of damage in structures is a challenging but critical ingredient in the path towards predictive or condition-based maintenance of high value structures. The use of acoustic emission time of arrival mapping is a promising approach to this challenge, but is severely hindered by the need to collect a dense set of artificial acoustic emission measurements across the structure, resulting in a lengthy and often impractical data acquisition process. In this paper, we consider the use of physics-informed Gaussian processes for learning these maps to alleviate this problem. In the approach, the Gaussian process is constrained to the physical domain such that information relating to the geometry and boundary conditions of the structure are embedded directly into the learning process, returning a model that guarantees that any predictions made satisfy physically-consistent behaviour at the boundary. A number of scenarios that arise when training measurement acquisition is limited, including where training data are sparse, and also of limited coverage over the structure of interest. Using a complex plate-like structure as an experimental case study, we show that our approach significantly reduces the burden of data collection, where it is seen that incorporation of boundary condition knowledge significantly improves predictive accuracy as training observations are reduced, particularly when training measurements are not available across all parts of the structure.