Azad Muhammad Muzammil, Munyaneza Olivier, Jung Jaehyun, Sohn Jung Woo, Han Jang-Woo, Kim Heung Soo
Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea.
Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Sensors (Basel). 2024 Dec 17;24(24):8057. doi: 10.3390/s24248057.
In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures. In contrast, this study aims to perform damage detection, severity assessment, and localization using independent DL models. Three DL models, namely the artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU), are compared. To assess their damage detection and localization capabilities. Moreover, zero-mean Gaussian noise is introduced as a data augmentation technique to address the variability and noise inherent in LW signals, improving the generalization capability of the DL models. The proposed framework is validated on a composite plate with four piezoelectric transducers, one at each corner, and achieves high accuracy in both damage localization and severity assessment, offering an effective solution for real-time structural health monitoring. This dual-function approach provides a scalable data-driven method to evaluate composite structures, with applications in predictive maintenance and reliability assurance in critical engineering systems.
在复合结构中，准确识别和定位损伤对于在航空、土木和机械工程等领域的应用中保持结构完整性至关重要。本研究提出了一种深度学习（DL）辅助框架，用于使用兰姆波（LW）同时对复合结构中的损伤进行定位和严重程度评估。以往的研究往往侧重于复合结构中的损伤检测或定位。相比之下，本研究旨在使用独立的深度学习模型进行损伤检测、严重程度评估和定位。比较了三种深度学习模型，即人工神经网络（ANN）、卷积神经网络（CNN）和门控循环单元（GRU），以评估它们的损伤检测和定位能力。此外，引入零均值高斯噪声作为数据增强技术，以解决兰姆波信号中固有的变异性和噪声问题，提高深度学习模型的泛化能力。所提出的框架在一个带有四个压电换能器的复合板上得到验证，每个角一个，在损伤定位和严重程度评估方面都达到了高精度，为实时结构健康监测提供了一种有效的解决方案。这种双功能方法提供了一种可扩展的数据驱动方法来评估复合结构，可应用于关键工程系统的预测性维护和可靠性保证。
