Structural Health Monitoring (SHM), i.e. the action of monitoring structures in real-time and in an automated manner, is a major challenge in several industrial fields such as aeronautic. SHM is by nature a very high dimensional data-driven problem that possesses several specificities when addressed as a machine learning problem. First of all data in damaged cases are rare and very costly as the generation of damaged data is not always possible and simulations are not reliable especially when dealing with complex structures. SHM is thus by nature an unsupervised problem. Furthermore, any incoming sample should be instantaneously clustered and handcrafted damage indexes are commonly used as a first dimension reduction step due to large datasets to be processed. As a consequence, unsupervised dimensionality reduction (DR) techniques that project very high dimensional data into a two or three-dimensional space (such as t-SNE or UMAP) are very appealing in such a context. However, these methods suffer from one major drawback which is that they are unable to cluster any unknown incoming sample. To solve this we propose to add inductive abilities to these well know methods by associating their projection bases with Deep Neural Networks (DNNs). The resulting DNNs are then able to cluster any incoming unknown samples. Based on those tools, a SHM methodology allowing for unsupervised damage clustering with dimensionality reduction is presented here. To demonstrate the effectiveness of the method, results of damage classification on large experimental data sets coming from complex aeronautical composite structures monitored through Lamb waves are shown. Furthermore, several DR techniques have been benchmarked and recommendations are derived. It is demonstrated that the use of raw Lamb wave signals instead of the associated damage indexes is more effective. This non-intuitive result helps to reduce the gap between laboratory research and the actual start-up of SHM activities in industrial applications.

结构健康监测（SHM），即以自动化方式实时监测结构的行为，是航空工业等多个工业领域的主要挑战。SHM本质上是一个非常高维的数据驱动问题，在作为机器学习问题解决时，它具有多种特性。首先，在损坏情况下的数据很少且非常昂贵，因为并非总是可能生成损坏的数据，并且仿真不可靠，尤其是在处理复杂结构时。因此，SHM本质上是一个不受监督的问题。此外，任何传入的样本都应立即进行聚类，并且由于要处理的数据集较大，通常将手工制作的破坏指数用作第一个降维步骤。作为结果，在这种情况下，将超高维数据投影到二维或三维空间（例如t-SNE或UMAP）的无监督降维（DR）技术非常有吸引力。但是，这些方法的一个主要缺点是无法将任何未知的传入样本聚类。为了解决这个问题，我们建议通过将其投影基础与深度神经网络（DNN）关联，将归纳能力添加到这些众所周知的方法中。然后，生成的DNN可以对任何传入的未知样本进行聚类。在这些工具的基础上，本文介绍了一种SHM方法，该方法可在无监督的情况下进行聚类并降低尺寸。为了证明该方法的有效性，显示了通过兰姆波监测的复杂航空复合结构对大型实验数据集进行的损伤分类结果。此外，已经对几种灾难恢复技术进行了基准测试，并提出了一些建议。结果表明，使用原始的兰姆波信号代替相关的损伤指数更为有效。这种非直觉的结果有助于减小实验室研究与工业应用中SHM活动的实际启动之间的差距。