Department of Aeronautics, Imperial College London, Exhibition Road, South Kensington, London SW7 2AZ, UK.
Sensors (Basel). 2022 Jun 9;22(12):4370. doi: 10.3390/s22124370.
This paper proposes a new method of impact classification for a Structural Health Monitoring system through the use of Self-Attention, the central building block of the Transformer neural network. As a topical and highly promising neural network architecture, the Transformer has the potential to greatly improve the speed and robustness of impact detection. This paper investigates the suitability of this new network, confronting the advantages and disadvantages offered by the Transformer and a well-known and established neural network for impact detection, the Convolutional Neural Network (CNN). The comparison is undertaken on performance, scalability, and computational time. The inputs to the networks were created using a data transformation technique, which transforms the raw time series data collected from the network of piezoelectric sensors, installed on a composite panel, through the use of Fourier Transform. It is demonstrated that the Transformer method reduces the computational complexity of the impact detection significantly, while achieving excellent prediction results.
本文提出了一种新的结构健康监测系统冲击分类方法，通过使用自注意力机制，即 Transformer 神经网络的核心构建块。作为一种热门且极具前景的神经网络架构，Transformer 有望极大地提高冲击检测的速度和鲁棒性。本文研究了这种新网络的适用性，对比了 Transformer 和一种用于冲击检测的知名且成熟的神经网络（卷积神经网络 (CNN)）的优缺点。比较了性能、可扩展性和计算时间。网络的输入是使用数据变换技术创建的，该技术通过使用傅里叶变换对安装在复合板上的压电传感器网络采集的原始时间序列数据进行转换。结果表明，Transformer 方法显著降低了冲击检测的计算复杂度，同时实现了优异的预测结果。
