Abstract
Data used for training structural health monitoring (SHM) systems are expensive and often impractical to obtain, particularly labelled data. Population-based SHM presents a potential solution to this issue by considering the available data across a population of structures. However, differences between structures will mean the training and testing distributions will differ; thus, conventional machine learning methods cannot be expected to generalise between structures. To address this issue, transfer learning (TL), can be used to leverage information across related domains. An important consideration is that the lack of labels in the target domain limits data-based metrics to quantifying the discrepancy between the marginal distributions. Thus, a prerequisite for the application of typical unsupervised TL methods is to identify suitable source structures (domains), and a set of features, for which the conditional distributions are related to the target structure. Generally, the selection of domains and features is reliant on domain expertise; however, for complex mechanisms, such as the influence of damage on the dynamic response of a structure, this task is not trivial. In this paper, knowledge of physics is leveraged to select more similar features, the modal assurance criterion (MAC) is used to quantify the correspondence between the modes of healthy structures. The MAC is shown to have high correspondence with a supervised metric that measures joint-distribution similarity, which is the primary indicator of whether a classifier will generalise between domains. The MAC is proposed as a measure for selecting a set of features that behave consistently across domains when subjected to damage, i.e. features with invariance in the conditional distributions. This approach is demonstrated on numerical and experimental case studies to verify its effectiveness in various applications.
Abstract (translated)
用于训练结构健康监测（SHM）系统的数据既昂贵又难以获取，尤其是带有标签的数据。基于群体的SHM为解决这一问题提供了潜在方案，通过考虑一组建筑物可用的数据来实现。然而，不同建筑之间的差异意味着训练和测试分布将有所不同；因此，传统的机器学习方法无法期望在不同的建筑之间泛化。为了应对这个问题，可以使用迁移学习（TL）来利用相关领域的信息。重要的一点是，在目标领域中缺乏标签限制了数据驱动的度量标准只能量化边际分布间的差异。因此，典型无监督TL方法的应用前提是要识别合适的源结构（领域），以及一组条件分布与目标结构相关的特征。通常，域和特征的选择依赖于专业知识；然而，对于复杂的机制，例如损伤对建筑动态响应的影响，这一任务并不简单。 本文利用物理学知识来选择更具相似性的特征，并使用模态保证准则（MAC）量化健康建筑物模式之间的对应关系。研究表明，MAC与衡量联合分布相似度的监督度量具有高度一致性，这是判断分类器是否能在不同领域之间泛化的主要指标。提出将MAC作为一种度量标准，用于选取在受到损伤时跨域行为一致的一组特征，即条件分布不变性的特征。通过数值和实验案例研究证明了该方法在各种应用中的有效性。
URL
https://arxiv.org/abs/2507.19519
