失速和喘振是发动机经常遇到的两种气动失稳现象，失速表现为低能量气团沿周向的旋转运动，喘振表现为轴向气流压力大幅度波动，发动机一旦发生气动失稳不能及时退出，可能导致发动机性能急剧恶化，甚至损坏发动机。当前零部件试车台采用的防喘系统是基于时域频域特征向量进行辨识判定，存在响应时间长、对失速不敏感的问题。随着发动机主动控制技术发展，需要更加精确地实时监测失稳初始扰动波信号，对失速辨识算法的快速响应提出了更高要求。本文提出了一种基于功率谱互相关的失速辨识算法，通过计算两个传感器通道之间的相关系数，在发动机进入旋转失速状态后及时发出警报，有效提前了失速预警时间，为发动机主动控制提供技术基础。
Stall and surge represent two prevalent forms of aerodynamic instability in engine. Stall is characterized by the circumferential propagation of rotating stall cells, while surge manifests as large-amplitude axial oscillations in pressure and mass flow. Failure to promptly mitigate such instabilities once initiated can lead to a rapid degradation in engine performance and, in severe cases, catastrophic mechanical damage.Current stall/surge detection or prevention systems deployed on component test facilities typically rely on feature vectors extracted from time- and frequency-domain signal analyses for instability detection. However, these conventional approaches often exhibit limited responsiveness and insufficient sensitivity to incipient stall events.In light of recent advances in active engine control methodologies, there is a growing need for high-fidelity, real-time monitoring of the initial perturbation waves associated with aerodynamic instability. This demand necessitates stall detection algorithms with enhanced speed and accuracy in identifying the onset of instability.To address this challenge, this paper presents a stall identification algorithm based on cross-correlation of power spectral densities. By computing the correlation coefficient between signals acquired from two spatially separated sensor channels, the proposed method enables timely detection of stall or surge inception and triggers an early warning. The algorithm effectively advances the lead time for stall prediction, thereby establishing a critical technical foundation for the implementation of active instability control strategies in engine.
[1] 刘大响. 航空燃气涡轮发动机稳定性设计与评定技术[M]. 北京: 航空工业出版社, 2004.
[2] GJB 4051–2000, 航空燃气涡轮发动机防喘系统通用规范[S].
[3] DHINGRA M, NEUMEIER Y, PRASAD J. Stall and surge precursors in axial compressors[R]. AIAA Paper 2003-4425, 2003.
[4] TAHARA N, KUROSAKI M, OHTA Y, et al. Early stall warning technique for axial-flow compressors[J]. ASME Journal of Turbomachinery, 2007, 129(3): 375–384.
[5] 李继超, 童志庭, 林峰, 等. 基于互相关分析的前失速先兆检测分析[J]. 航空学报, 2013, 34(1): 28–36.
[6] 范忠岗, 刘洋, 巴顿, 等. 周向畸变进气下轴流压气机的失稳预报方法研究[J]. 推进技术, 2025, 46(4): 2405017.
[7] 刘正先, 王生玲. 空间傅里叶分析在离心叶轮失速信号识别中的应用[J]. 天津大学学报(自然科学与工程技术版), 2019, 52(4): 1–8.
[8] 王颖. 一种基于相位度量的低速压气机前失速特征分析方法[J]. 推进技术, 2025, 46(待定).
[9] 胡竞涛. 多级高速压气机喘振失稳的确定学习建模与预警[J]. 航空动力学报, 2025, 40(待定).
[10] 张海波. 基于压力信号分析的轴流式压气机失速预测[J]. 航空动力学报, 2025, 40(待定).
[11] 孙大坤. 进气畸变下两级跨声风扇失速预测与试验研究[J]. 推进技术, 2025, 46(待定).
[12] 全福祥. 航空发动机失稳预测和喘振主动控制研究[D]. 大连: 大连理工大学, 2025.
[13] 刘卓. 航空发动机失速喘振辅助判别方法研究[J]. 计量与测试技术, 2019, 46(3): 70–72.
[14] HOENEN H, ARNOLD T. Development of a surge prediction system for multi stage axial compressors[C]// Proceedings of the International Gas Turbine Congress (IGTC 2003). Tokyo, Japan, 2003: TS-040.
[15] 向宏辉, 侯敏杰, 杨荣菲. 基于希尔伯特-黄与小波变换的压气机失速信号分析方法[J]. 燃气涡轮试验与研究, 2015, 28(5): 1–5.
[16] LENHERR C L, KALFAS A I, ABHARI R S. High temperature fast response aerodynamic probe[R]. ASME Paper GT2010-23010, 2010.
[17] ENGLUND D R, RICHARDS W B. The infinite line pressure probe[R]. NASA Technical Memorandum NASA TM-83582, 1984.
[18] 徐维超.相关系数研究综述[J].广东工业大学学报, 2012, 29(3):6.DOI:10.3969/j.issn.1007-7162.2012.03.002..
[19] FISHER R A. Statistical methods, experimental design, and scientific inference[M]. New York: Oxford University Press, 1990.
[20] KENDALL M, GIBBONS J D. Rank correlation methods[M]. 5th ed. New York: Oxford University Press, 1990.
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