School of Aerospace Engineering, Xiamen University, Xiamen 361102, China.
Sensors (Basel). 2021 Feb 19;21(4):1452. doi: 10.3390/s21041452.
Impact brings great threat to the composite structures that are extensively used in an aircraft. Therefore, it is necessary to develop an accurate and reliable impact monitoring method. In this paper, fiber Bragg grating (FBG) sensors are embedded in unidirectional carbon fiber reinforced plastics (CFRPs) during the manufacturing process to monitor the strain that is related to the elastic modulus and the state of resin. After that, an advanced impact identification model is proposed. Support vector regression (SVR) and a back propagation (BP) neural network are combined appropriately in this stacking-based ensemble learning model. Then, the model is trained and tested through hundreds of impacts, and the corresponding strain responses are recorded by the embedded FBG sensors. Finally, the performances of different models are compared, and the influence of the time of arrival (ToA) on the neural network is also explored. The results show that compared with a single neural network, ensemble learning has a better capability in impact identification.
冲击给广泛应用于飞机的复合材料结构带来了巨大的威胁。因此，有必要开发一种准确可靠的冲击监测方法。在本文中，在制造过程中将光纤布拉格光栅（FBG）传感器嵌入单向碳纤维增强塑料（CFRP）中，以监测与弹性模量和树脂状态相关的应变。之后，提出了一种先进的冲击识别模型。在这个基于堆叠的集成学习模型中，适当结合了支持向量回归（SVR）和反向传播（BP）神经网络。然后，通过数百次冲击对模型进行训练和测试，并通过嵌入的 FBG 传感器记录相应的应变响应。最后，比较了不同模型的性能，并探讨了到达时间（ToA）对神经网络的影响。结果表明，与单个神经网络相比，集成学习在冲击识别方面具有更好的性能。
