In the present work, an active source localization strategy is proposed. The presence of active sources in a waveguide can have several reasons, such as crack initiation or internal friction. In this study, the active source is represented by an impact event. A steel ball is dropped on an aluminum plate at different positions. Elastic waves are excited and will propagate through the plate. The wave response is acquired by a piezoelectric sensor network, which is attached to the plate. After performing numerical and physical experiments, enough data are collected in order to train an artificial neural network and a support vector machine. Those machine learning algorithms will predict the impact position based on the wave response of each sensor, while only numerical data from the finite element simulations are used to train both methods. After the training process is completed, the algorithms are applied to experimental data. A good agreement between reference and predicted results proves that the wave responses at the piezoelectric transducers contain sufficient information in order to localize the impact position precisely.

中文翻译：

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基于机器学习的波导主动源定位

在目前的工作中，提出了一种主动源定位策略。波导中存在有源源可能有多种原因，例如裂纹萌生或内部摩擦。在本研究中，活动源由撞击事件表示。一个钢球在不同位置落在铝板上。弹性波被激发并将通过板传播。波响应由贴在板上的压电传感器网络获取。在进行数值和物理实验后，收集了足够的数据以训练人工神经网络和支持向量机。这些机器学习算法将根据每个传感器的波响应预测撞击位置，而仅使用来自有限元模拟的数值数据来训练这两种方法。训练过程完成后，将算法应用于实验数据。参考结果和预测结果之间的良好一致性证明压电换能器处的波响应包含足够的信息，以便精确定位撞击位置。