轴流压气机旋转失速建模与检测Ⅱ:基于北航低速压气机试验台的实验研究

Modeling and detection of rotating stall in axial flow compressors,Ⅱ:Experimental study for a low-speed compressor in Beihang University

轴流压气机旋转失速和喘振的提前检测对于提高压气机工作效率和稳定性具有重要的意义.本文以北京航空航天大学航空发动机重点实验室的低速轴流压气机实验台为研究对象,基于确定学习理论及动态模式识别方法,开展旋转失速初始扰动近似准确建模和快速检测研究.首先,在压气机机匣壁面周向布置多个动态压力传感器,获取压气机失速前和失速先兆的动态压力信号,基于确定学习理论对旋转失速初始扰动的内部系统动态进行建模;其次,基于以上建模,利用微小振动故障检测方法实现对旋转失速的离线和在线提前检测.实验结果表明,本文所提方法能够在不同转速情况下,提前0.3 s^1 s实现对旋转失速的实时在线检测.

Early detection of rotating stall and surge in axial flow compressors is of great importance for improvingthe working efficiency and stability of the compressor. Based on deterministic learning （DL） theory and dynamical patternrecognition, this paper presents experimental research for approximately accurate modeling and rapid detection of stallprecursors, and then employs a low-speed axial flow compressor test rig of Beihang University for online experimentalverification. Firstly, by installing high response dynamic pressure transducers arranged circumferentially around the casingof the axial compressor, the dynamic pressure data are collected. Based on deterministic learning theory, the systemdynamics underlying prestall and stall inception pattems are identified. Secondly, based on modeling results, rapid detectionof small oscillation faults is used to perform the detection of stall precursors. Sufficient online experiments are conductedto investigate the efficiency of the approach. Results show that, in different working speeds, this approach successfullydetects inception signal of aerodynamic instability of the compressor 0.3 s-l s in advance to the start of rotating stalls.