Unsupervised damage clustering in complex aeronautical composite structures monitored by Lamb waves: An inductive approach
Unsupervised damage clustering in complex aeronautical composite structures monitored by Lamb waves: An inductive approach
复制标题
DOI：
10.1016/j.engappai.2020.104099
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
A. Rahbari;M. Rébillat;N. Mechbal;S. Canu
中科院分区：
文献类型：
--
作者：
A. Rahbari;M. Rébillat;N. Mechbal;S. Canu
结构健康监测(SHM)，即对结构进行实时、自动化的监测，是航空等工业领域的一大挑战。SHM本质上是一个非常高维的数据驱动问题，当作为机器学习问题来处理时，它具有几个特殊性。首先，破损情况下的数据很少，而且成本非常高，因为破损数据的产生并不总是可能的，模拟也不可靠，特别是在处理复杂结构时。因此，SHM本质上是一个无人监督的问题。此外，由于要处理的数据集很大，因此任何传入的样本都应该立即进行聚类，并且通常使用手工创建的损伤指数作为第一个降维步骤。因此，将高维数据投影到二维或三维空间(如t-SNE或UMAP)的无监督降维(DR)技术在这样的背景下非常有吸引力。然而，这些方法有一个主要缺点，那就是它们不能对任何未知的输入样本进行聚类。为了解决这一问题，我们建议通过将它们的投影基与深度神经网络(DNN)相关联来增加这些众所周知的方法的归纳能力。然后，得到的DNN能够对任何传入的未知样本进行聚类。在这些工具的基础上，本文提出了一种基于降维的无监督损伤聚类的结构健康监测方法。为了验证该方法的有效性，给出了对兰姆波监测的复杂航空复合材料结构的大型实验数据集进行损伤分类的结果。此外，还对几种灾难恢复技术进行了基准测试，并得出了建议。结果表明，用原始兰姆波信号代替相应的损伤指标更为有效。这一非直观的结果有助于缩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。
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.