Performances are a key concern in aerospace vehicles, requiring safer structures with as little consumption as possible. Composite materials replaced aluminum alloys even in primary aerospace structures to achieve higher performances with lighter components. However, random events such as low-velocity impacts may induce damages that are typically more dangerous and mostly not visible than metals. The damage tolerance (DT) approach is adopted for the fatigue design of aircraft, but fracture mechanisms and propagation of failure prediction in composite structures are much more challenging. Consequently, the DT approach is still costly for these types of structures. It can be achieved only through expensive experimental testing and a drastic reduction of allowable stress levels and maintenance intervals by applying scattering factors due to the uncertainties involved in their original estimations. Structural health monitoring (SHM) systems deal mainly with sensorised structures providing signals related to their “load and health status” to reduce maintenance and weights. At the same time, the use of Deep Neural Networks (DNNs) based on strategic engineering criteria, for instance, may represent an effective and efficient analysis tool to promote faster data analysis and classification. In the field of aircraft maintenance, this approach may lead, for example, to a faster awareness of an aircraft/fleet situation or predict failures. Deep learning-based networks provide automatic feature extraction at different levels of abstraction. With the universal function approximation property of neural networks, it learns the inverse mapping from input space (signals) to target space (damage classes). Starting from the well-established Structural Health Monitoring (SHM) technologies, a network of distributed sensors embedded throughout the structure could be used for real-time structural monitoring and data acquisition. Structural data will constitute an enormous amount of information that can be adequately filtered with the help of specific DNNs designed and trained for the structural context and aimed to classify and identify significant parameters. The authors have collaborated for some years to collect wave propagation signals through experimental tests and validated numerical models of healthy and damaged composite structures, and developed machine learning algorithms (mainly dense and convolutional neural networks) aimed at signal classification and analysis for damage detection and localization. This paper presents a brief review of relevant works about SHM employing Machine Learning methodologies and summarizes the most promising approaches developed during the last years jointly by the two research groups and presents a critical analysis of obtained results and subsequent future activities.

中文翻译：

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通过波传播信号分析来检测复合材料板分层的机器学习算法：回顾、经验和结果


性能是航空航天器的一个关键问题，需要更安全的结构和尽可能少的消耗。即使在主要的航空航天结构中，复合材料也取代了铝合金，以使用更轻的部件实现更高的性能。然而，诸如低速撞击之类的随机事件可能会引起通常比金属更危险且大多不可见的损坏。飞机的疲劳设计采用损伤容限（DT）方法，但复合材料结构的断裂机制和失效扩展预测更具挑战性。因此，对于这些类型的结构来说，DT 方法的成本仍然很高。由于原始估计中存在不确定性，只能通过昂贵的实验测试以及应用散射因子来大幅降低允许应力水平和维护间隔来实现。结构健康监测（SHM）系统主要处理传感结构，提供与其“负载和健康状态”相关的信号，以减少维护和重量。与此同时，例如，使用基于战略工程标准的深度神经网络（DNN）可能是一种有效且高效的分析工具，可以促进更快的数据分析和分类。例如，在飞机维护领域，这种方法可以更快地了解飞机/机队情况或预测故障。基于深度学习的网络提供不同抽象级别的自动特征提取。利用神经网络的通用函数逼近特性，它学习从输入空间（信号）到目标空间（损坏类别）的逆映射。 从成熟的结构健康监测（SHM）技术开始，嵌入整个结构的分布式传感器网络可用于实时结构监测和数据采集。结构数据将构成大量信息，可以借助针对结构上下文设计和训练的特定 DNN 进行充分过滤，旨在对重要参数进行分类和识别。作者多年来一直合作，通过实验测试收集波传播信号，并验证健康和受损复合结构的数值模型，并开发了机器学习算法（主要是密集和卷积神经网络），旨在进行信号分类和分析，以进行损伤检测和定位。本文简要回顾了 SHM 采用机器学习方法的相关工作，总结了过去几年两个研究小组共同开发的最有前途的方法，并对所获得的结果和后续的未来活动进行了批判性分析。