Batista Gracieth Cavalcanti, Zetterling Carl-Mikael, Öberg Johnny, Saotome Osamu
KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, 164 40 Kista, Sweden.
ITA Technological Institute of Aeronautics, Electronic and Computer Engineering, São José dos Campos 12228-900, SP, Brazil.
Sensors (Basel). 2024 Dec 6;24(23):7817. doi: 10.3390/s24237817.
There is extensive use of nondestructive test (NDT) inspections on aircraft, and many techniques nowadays exist to inspect failures and cracks in their structures. Moreover, NDT inspections are part of a more general structural health monitoring (SHM) system, where cutting-edge technologies are needed as powerful resources to achieve high performance. The high-performance aspects of SHM systems are response time, power consumption, and usability, which are difficult to achieve because of the system's complexity. Then, it is even more challenging to develop a real-time low-power SHM system. Today, the ideal process is for structural health information extraction to be completed on the flight; however, the defects and damage are quantitatively made offline and on the ground, and sometimes, the respective procedure test is applied later on the ground, after the flight. For this reason, the present paper introduces an FPGA-based intelligent SHM system that processes Lamb wave signals using piezoelectric sensors to detect, classify, and locate damage in composite structures. The system employs machine learning (ML), specifically support vector machines (SVM), to classify damage while addressing outlier challenges with the Mahalanobis distance during the classification phase. To process the complex Lamb wave signals, the system incorporates well-known signal processing (DSP) techniques, including power spectrum density (PSD), wavelet transform, and Principal Component Analysis (PCA), for noise reduction, feature extraction, and data compression. These techniques enable the system to handle material anisotropy and mitigate the effects of edge reflections and mode conversions. Damage is quantitatively evaluated with classification accuracies of 96.25% for internal defects and 97.5% for external defects, with localization achieved by associating receiver positions with damage occurrence. This robust system is validated through experiments and demonstrates its potential for real-time applications in aerospace composite structures, addressing challenges related to material complexity, outliers, and scalable hardware implementation for larger sensor networks.
无损检测（NDT）在飞机上得到了广泛应用，如今有许多技术可用于检测飞机结构中的故障和裂纹。此外，无损检测是更通用的结构健康监测（SHM）系统的一部分，其中需要前沿技术作为强大资源以实现高性能。结构健康监测系统的高性能方面包括响应时间、功耗和可用性，由于系统的复杂性，这些方面难以实现。因此，开发实时低功耗的结构健康监测系统更具挑战性。如今，理想的过程是在飞行中完成结构健康信息提取；然而，缺陷和损伤是在地面离线定量的，有时在飞行后在地面上进行各自的程序测试。出于这个原因，本文介绍了一种基于现场可编程门阵列（FPGA）的智能结构健康监测系统，该系统使用压电传感器处理兰姆波信号，以检测、分类和定位复合材料结构中的损伤，并采用机器学习（ML），特别是支持向量机（SVM）进行损伤分类，同时在分类阶段利用马氏距离应对异常值挑战。为了处理复杂的兰姆波信号，该系统采用了包括功率谱密度（PSD）、小波变换和主成分分析（PCA）在内的知名信号处理（DSP）技术，用于降噪、特征提取和数据压缩。这些技术使系统能够处理材料各向异性，并减轻边缘反射和模式转换的影响。通过将接收器位置与损伤发生情况相关联实现定位，内部缺陷的分类准确率为96.25%，外部缺陷的分类准确率为97.5%，从而对损伤进行定量评估。这个强大的系统通过实验得到了验证，并展示了其在航空航天复合材料结构实时应用中的潜力，解决了与材料复杂性、异常值以及更大传感器网络的可扩展硬件实现相关的挑战。
