纤维增强树脂基复合材料(Fiber reinforced plastics,FRPs)因其高模高强、可设计强、耐腐蚀等优异性能,已广泛应用于航空航天等高性能领域。结构健康监测(Structural health mornitoring,SHM)对于保障复合材料结构的安全运行至关重要。随着人工智能技术的进步,机器学习方法在复合材料结构健康监测领域也得到快速发展,以数据驱动方法代替传统模型对结构状态进行判断,使得基于各类传感器的结构健康监测技术具有更高的准确性、鲁棒性及高效性。基于此,本文首先阐述了在复合材料结构健康监测领域中常用的机器学习算法,其次总结了机器学习方法在复合材料损伤模式识别、损伤位置识别和损伤程度识别几个方面的研究进展,最后讨论了基于机器学习的复合材料结构健康监测的未来发展趋势。
The use of fiber reinforced plastics (FRPs) is on the rise in aerospace and other high-tech fields, owing to their high specific strength/stiffness, design flexibility, and corrosion resistance. Structural health monitoring (SHM) is therefore crucial to ensure structural safety. Recently, machine learning has emerged as a promising approach for SHM of FRPs. Data-driven methods that employ machine learning algorithms have shown higher accuracy, robustness, and efficiency in SHM. After the brief introduction of the machine learning algorithms commonly used in SHM of FRPs, the applications of machine learning in SHM are reviewed mainly from the following aspects: Damage pattern recognition, damage localization, and damage degree recognition of FRPs. Finally, the future development trend of SHM based on machine learning in FRPs are discussed.