Aircraft structures are exposed to impact damage caused by debris and hail during their service life. One of the design concerns in composite structures is the resistance of layered surfaces to damage, which occurs from impacts with various foreign objects. Therefore, the impact localization and damage quantification of impacts should be studied and considered to address flight safety and to reduce costs associated with a regularly scheduled visual inspection. Since the structural components of the aircraft are large scale, visual inspection and monitoring are challenging and subject to human error. This paper presents a promising solution that can automatically detect and localize an impact that may occur during flight. To achieve this goal, acoustic emission (AE) is employed as an impact monitoring approach. Random forest and deep learning were adopted for training the source location models. An AE dataset was collected by conducting an impact experiment on a full-size thermoplastic aircraft elevator in a laboratory environment. A dataset consisting of AE parametric features and a dataset consisting of AE waveforms were assigned to a random forest classifier and deep learning network for the investigation of their applicability of impact source localization. The results obtained were compared using the source localization approach in previous research using a conventional artificial neural network. The analysis of results shows the random forest and deep learning leads to better event localization performance. In addition, the random forest model can provide the importance of features. By deleting the least important features, the storage required to save the input and the computing time for the random forest is greatly reduced, and an acceptable localization performance can still be obtained.

飞机结构在其使用寿命期间会受到碎片和冰雹造成的冲击损坏。复合结构的设计关注点之一是分层表面对损坏的抵抗力，这种损坏是由各种异物撞击引起的。因此，应研究和考虑影响的影响定位和损坏量化，以解决飞行安全问题并降低与定期安排的目视检查相关的成本。由于飞机的结构部件规模较大，目视检查和监控具有挑战性，并且容易出现人为错误。本文提出了一种很有前景的解决方案，可以自动检测和定位飞行过程中可能发生的撞击。为了实现这一目标，声发射 (AE) 被用作影响监测方法。采用随机森林和深度学习来训练源位置模型。AE 数据集是通过在实验室环境中对全尺寸热塑性飞机升降机进行撞击实验来收集的。由 AE 参数特征组成的数据集和由 AE 波形组成的数据集被分配给随机森林分类器和深度学习网络，以研究它们对冲击源定位的适用性。使用传统人工神经网络在先前研究中使用源定位方法对获得的结果进行比较。结果分析表明随机森林和深度学习导致更好的事件定位性能。此外，随机森林模型可以提供特征的重要性。通过删除最不重要的特征，