先进飞行器复合材料结构安全状态的多信息多指标融合辨识方法研究

The composite structures of advanced aircrafts are inevitably affected by unsafe factors such as fatigue, large overloading and impact during their long service life. Structural health monitoring(SHM) is an important technology to ensure the safe and reliable operation of the structures. However, existing SHM methods for composite structures still contain many bottlenecks, such as the incompleteness of safety state characterization under multiple failure modes, high ill-posedness of the inversion equations for state parameters, the difficulty of the reliable optimization of sensors layout with multiple objectives and constraints. This project will carry out the study on the construction of multi-state evaluation index for safety of composite structures under multi failure modes, the highly efficient and accurate solution for the monitoring information of structural safety states under low signal-to-noise ratio and the joint-layout optimization of multi-source sensors and the corresponding experiment validation. We will propose a safety assessment concept and method for the fusion of multi-state indices containing structural local damage, overall deformation and external loads, establish a multi-information interval fusion method for safety state inversion based on set membership identification and develop a hybrid global and local optimization algorithm suited with large-scale variables for state parameters identification and sensors layout optimization, in order to enhance the identification ability of structural health state, reduce the inadaptability of the state inversion and improve the accuracy of the damage identification. The existing theories and methods of SHM for composite structures will be improved to support the practical application in the safety state monitoring and maintenance decision of the composite structures of the advanced aircrafts.

先进飞行器复合材料结构在长期服役过程中不可避免受到疲劳、大过载、撞击等不安全因素影响，结构健康监测是保障其安全可靠运行的重要手段。然而，现有复合材料结构健康监测方法仍然存在多失效模式下安全状态表征欠完备、状态参数反演方程高不适定性和多目标多约束下传感器可靠性布局寻优困难等诸多瓶颈。本项目将开展复合材料结构多失效模式下安全性的多状态综合评估指标构建、低信噪比下结构安全状态监测信息的高效高精度求解、多源传感器联合布局优化与试验验证研究。拟提出一种集结构局部损伤、整体变形和外部载荷多状态指标融合的安全评估理念与方法，建立基于集员辨识安全状态反演的多信息区间融合方法，发展面向状态参数辨识和传感器布局的大规模变量全局/局部混合优化算法，以增强结构健康状态辨识度、降低状态反演不适定度、提高损伤识别精度。丰富和发展现有复合材料结构健康监测理论与方法，为先进飞行器结构安全状态监测和维护决策的应用提供支撑。

先进飞行器复合材料结构在长期服役中易受到疲劳、大过载、撞击等不安全因素影响，结构健康监测是保障其安全可靠运行的重要手段。然而，现有复合材料结构健康监测方法仍然存在多失效模式下安全状态表征欠完备、状态参数反演方程高不适定性和多目标多约束下传感器可靠性布局寻优困难等诸多瓶颈。本项目以复合材料力学、力学反问题、非概率不确定性和信息融合理论为研究基础，提出了复合材料结构安全状态的多信息多指标融合辨识方法。.本项目完成的主要研究包括：（1）完成了局部损伤模式的力学模型研究，实现了由宏观型损伤向细观型损伤的过渡；阐释了由局部应变监测推演出全局变形监测的数学机理，实现了整体变形指标的完备数学表达；通过融合结构多源传感信息，实现了对结构损伤、变形和载荷的安全状态表征。（2）聚焦于安全状态参数高效高精度辨识中的不适定性、高维度、非线性与低信噪比难点，以“先定位，后定量”为基本思想，首先完成了主被动融合下的复合材料结构多类型损伤状态反演方程的求解方法研究。进而逐级设计了低信噪比环境下损伤参数区间反演方法和基于集员辨识思想的精细化辨识方法。（3）借助于多类型传感信息，以安全状态监测的效果为标的，实现了综合考虑提升全局-局部监测效果的传感器优化布置，进而设计了不确定条件下虑及组员重要性的传感器布置策略。（4）以典型结构为验证对象，完成了对于损伤量化辨识、低信噪比环境下辨识算法测试、结构变形监测及布局优化设计的试验验证研究，证明了研究成果在真实世界中的有效性与正确性。.本项目解决了先进飞行器复合材料结构健康监测中面临的安全性能指标构建、安全状态高精度快速求解与多源传感器联合布局等关键科学问题，形成了面向飞行器复合材料结构健康监测的前瞻性、系统性解决方案，为飞行器领域数字化运维体系的建设提供了可行的理论参考和技术支持。.本项目形成研究成果包括SCI论文32篇，发明专利7项，专著1部，软件著作权1项，培养研究生9名。