针对航空压气机动叶片疲劳失效亟待解决的紧迫问题，瞄准非定常流激励下的动叶片在线监测与早期预警方法问题，开展流体激振下的叶片振动机理、叶片运行过程的在线监测、动应力反演、参数表征方法与故障诊断方法研究。从流体激振造成叶片振动机理的研究入手，结合流体动力学仿真与结构动力学分析，并在压气机模型级试验台进行验证，获取非定常流激励载荷传递与叶片动载荷的作用规律，阐明非定常流激励下动叶片的振动机理。在此基础上，进行动叶片特征提取方法的研究，建立动叶片运行状态参数与监测参数的映射关系，实现叶片运行过程中最佳的监测位置和监测参数的种类选取；开展表征叶片振动监测信号的解耦研究，建立叶片动应力反演方法；基于监测信号的特征参数提取方法研究，建立动叶片的状态识别方法。通过本项目的研究，将促进叶片在线监测与早期预警方法的发展，同时可以为燃气轮机、轴流压缩机、汽轮机等动叶片在线监测与早期预警的问题解决提供借鉴。

Rotor blade fatigue failure is a very urgent problem for air compressor, which could lead to serious catastrphe. This research aims at rotor blade condition monitoring and early warning problems to keep air compressor working in normal conditions. Blade vibration mechanism based on flow induced vibration, rotor blade oeprational condition monitoring, dynamic stress inversion, parameters charactersitics method, and fault diagnosis method are investigated in this research. Rotor blade mechanism with unsteady flow induced vibration is firstly investigated by using fluid dynamics simulation analysis and structural dynamics simulation analysis. Experimental verification is also launched on the model stage test-rig. The action law of the aerodynamic load transmission and the blade dynamic load are obtained, and blade vibration mechanism based on flow induced vibration characterization can be constructed. Based on those results, rotor blade feature extraction method will be carried on. The mapping relationship between blade operating state parameters and monitoring parameters is esttablished. Characteritic parameters for blade working condition are investigated to determine the best monitoring position and monitoring parameters. The decoupling of the rotor blade monitoring signal of will be carried out. The method of blade dynamic stress inversion will be established. As well, the pattern recognition method of blade can be established based on the characteristic parameter extraction method of the monitoring signal. This research will contribute to the development of rotor blade condition monitoring and early warning method. The investigation of this research will also be used as a reference to rotor blade condition monitoring and early warning problems solvement, such as gas turbine, axial compressor, steam turbines, and so on.