Flavio Dipietrangelo, Francesco Nicassio, Gennaro Scarselli
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引用次数: 0
摘要
本研究提出了一种用于遥控飞行器(RPV)飞机撞击检测的在役结构健康监测系统。该方法基于金属结构中兰姆波的传播,在金属结构上粘接了 Pb[ZrxTi1-x]O3 (PZT) 传感器,用于接收撞击事件引起的振动信号。所提出的方法可用于检测航空航天结构(即机身蒙皮和/或机翼面板)中的撞击。检测后,应用机器学习(ML)算法(多项式回归和神经网络)处理获取的超声波,以便根据飞行时间(ToF)和相对位置确定撞击的特征。对几个测试案例进行了研究:在没有外部噪音(实验室)和引入外部遥控发动机振动(工作条件)的情况下对 ML 模型进行了测试。此外,这项工作还介绍了基于树莓派(Raspberry Pi)的微型采集和数据处理设备的实施情况。就实际撞击位置与计算撞击位置之间的距离而言,实验室结果与飞行中结果之间取得了良好的一致性。
SHM Implementation on a RPV Airplane Model Based on Machine Learning for Impact Detection
In this study, an on-working structural health monitoring system for impact detection on remote piloted vehicle (RPV) airplane is proposed. The approach is based on the propagation of Lamb waves in metallic structures on which Pb[ZrxTi1−x]O3 (PZT) sensors are bonded for receiving vibrational signals due to impact events. The proposed method can be used to detect impacts in aerospace structures, i.e. skin fuselage and/or wing panels. After the detection, machine learning (ML) algorithms (polynomial regression and neural networks) are applied for processing the acquired ultrasounds waves in order to characterise the impacts, in terms of time of flight (ToF) and relative location. Several test cases are studied: the ML models are tested both without external noise (in laboratory) and introducing external RC engine vibration (on-working conditions). Furthermore, this work presents the implementation of a mini-equipment for acquisition and data processing based on Raspberry Pi. A good agreement between laboratory and in-flight results is achieved, in terms of distance between the actual and calculated impact location.