基于压缩感知的叶端定时欠采样多频叶片振动盲重构研究

doi:10.3901/JME.2019.13.113

机械工程学报 ›› 2019, Vol. 55 ›› Issue (13): 113-121.doi: 10.3901/JME.2019.13.113

• 特邀专栏：航空发动机健康监测与故障诊断 • 上一篇下一篇

基于压缩感知的叶端定时欠采样多频叶片振动盲重构研究

徐海龙^1,2, 杨拥民¹, 胡海峰¹, 官凤娇¹, 陈仲生¹

1. 国防科技大学装备综合保障国防科技重点实验室长沙 410073;
2. 株洲中车时代电气股份有限公司株洲 412001

收稿日期:2018-07-19 修回日期:2018-12-07 出版日期:2019-07-05 发布日期:2019-07-05
通讯作者: 杨拥民(通信作者),男,1966年出生,博士,教授,博士研究生导师。主要研究方向为状态监控与故障诊断,信号处理等。E-mail:yangyongmin@163.com
作者简介:徐海龙,男,1989年出生,博士。主要研究方向航空发动机压气机叶片动力学分析及裂纹检测。E-mail:xhlym1@163.com
基金资助:
国家重点基础研究发展计划资助项目（973计划，2015CB057400）。

Compressed Sensing-based Blind Reconstruction of Multi-frequency Blade Vibration from Under-sampled BTT Signals

XU Hailong^1,2, YANG Yongmin¹, HU Haifeng¹, GUAN Fengjiao¹, CHEN Zhongsheng¹

1. Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha 410073;
2. Zhuzhou CRRC Times Electric Co., Ltd., Zhuzhou 412001

Received:2018-07-19 Revised:2018-12-07 Online:2019-07-05 Published:2019-07-05

摘要/Abstract

摘要： 旋转叶片是航空发动机的核心部件，长期工作在复杂的工况下承受交变应力，容易产生振动而导致疲劳失效，因此在线监测叶片振动对发动机运行安全具有十分重要的工程意义。针对传统的接触式应变法无法同时测量所有叶片振动且布线复杂存在安全隐患的问题，采用叶端定时（Blade tip-timing，BTT）非接触方法对叶片振动进行实时在线监测。但是BTT测量信号受叶顶传感器安装限制属于典型的欠采样信号，而叶片由于气动激励及微小裂纹的非线性导致其叶端出现多频振动响应，因此利用欠采样的BTT信号获取未知多频叶片振动是目前遇到的巨大挑战。提出采用压缩感知方法解决BTT欠采样多频叶片振动盲重构。首先分析叶片在气动激励下的多频响应；然后建立BTT测量的压缩感知模型，并采用多重信号分类（Multiplesignal classification，MUSIC）算法进行求解；最后通过数值仿真，并结合旋转叶片BTT测振实验台验证了压缩感知理论解决BTT欠采样多频叶片振动盲重构难题，实现旋转叶片振动BTT非接触在线测量。

关键词: 航发发动机, 欠采样信号重构, 压缩感知, 叶端定时, 叶片振动监测

Abstract: The rotated blade is the core component of aeroengine. However, the blades work in complex working conditions for a long time and are subjected to alternating stresses, which make balde vibrations and result in fatigue failure. Therefore, on-line vibration monitoring is of great engineering significance to operation safety for aeroengine. As the traditional contact strain method cannot measure vibration of all blades at the same time and the arrangement of wire is complex which may bring security risks, Blade tip-timing (BTT) technology is used for the blade real time online vibration monitoring. However, BTT signals are typically under-sampled signals by limited installation of BTT sensors. In addition, blade vibrations are always multi-frequency with less prior knowledge due to crack nonlinearity and complicated aerodynamic excitations. So it is a big challenge that unknowns multi-frequency blade vibrations are reconstructed from BTT under-sampled measurements. The compressed sensing (CS) theory is proposed to reconstruct unknown multi-band blade vibrations from under-sampled BTT measurements. Firstly, it is analyzed that multi-frequency blade vibrations are excited by aerodynamic excitations. Then, The CS model of BTT measurement is built and the MUSIC algorithm is selected to solve the model. Finally, numerical simulations and vibrations measurement of rotated blades by BTT method are used to validate the proposed method.

Key words: aeroengine, blade tip-timing, blade vibration monitoring, compressed sensing, under-sampled signal reconstruction

中图分类号:

V232

徐海龙, 杨拥民, 胡海峰, 官凤娇, 陈仲生. 基于压缩感知的叶端定时欠采样多频叶片振动盲重构研究[J]. 机械工程学报, 2019, 55(13): 113-121.

XU Hailong, YANG Yongmin, HU Haifeng, GUAN Fengjiao, CHEN Zhongsheng. Compressed Sensing-based Blind Reconstruction of Multi-frequency Blade Vibration from Under-sampled BTT Signals[J]. Journal of Mechanical Engineering, 2019, 55(13): 113-121.

参考文献

[1] TCHERNIAK D. Rotor anisotropy as a blade damage indicator for wind turbine structural health monitoring systems[J]. Mechanical Systems and Signal Processing, 2016, 74:183-198.
[2] DONATO V, BANNISTER R L, DEMARTINI J F. Measuring blade vibration of large low pressure steam turbine[J]. Power Engineering, 1981, 85(3):68-69.
[3] PAN M H, YANG Y M, GUAN F J, et al. Sparse representation based frequency detection and uncertainty reduction in blade tip timing measurement for multi-mode blade vibration monitoring[J]. Sensors, 2017, 17(8).
[4] LIN J, HU Z, CHEN Z S, et al. Sparse reconstruction of blade tip-timing signals for multi-mode blade vibration monitoring[J]. Mechanical Systems and Signal Processing, 2016, 81:250-258.
[5] 郭浩天,段发阶,汪猛. 基于叶尖定时单参数法的叶片共振倍频数辨识[J]. 天津大学学报, 2016, 49(9):951-955. GUO Haotian, DUAN Fajie, WANG Meng, Engine order identification based on blade tip-timing single parameter method[J]. Journal of Tianjin University, 2016, 49(9):951-955.
[6] RIGOSI G, BATTIATO G, BERRUTI T M. Synchronous vibration parameters identification by tip timing measurements[J]. Mechanics Research Communications, 2016, 79:7-14.
[7] CARRINGTON I B, WRIGHT J R, COOPER J E, et al. A comparison of blade tip timing data analysis methods[J]. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering, 2001, 215(5):301-312.
[8] 段发阶,张玉贵,欧阳涛,等. 航空发动机旋转叶片振动监测系统研究[J]. 光学与光电技术, 2008, 6(1):48-51. DUAN Fajie, ZHANG Yugui, OUYANG Tao, et al. Research on rotating blade vibration monitoring system for aeroengine[J]. Optics and Optoelectronic Technology, 2008, 6(1):48-51.
[9] 王宇华. 高速旋转叶片振动叶端定时测量方法和系统研究[D]. 天津:天津大学, 2004. WANG Yuhua. Research of measuring method and system using tip-timing for high-speed rotating blade vibration[D]. Tianjin:Tianjin University, 2004.
[10] 李孟麟,段发阶,欧阳涛,等. 基于叶尖定时的旋转机械叶片振动频率辨识ESPRIT方法[J]. 振动与冲击, 2010, 29(12):18-21. LI Menglin, DUAN Fajie, OUYANG Tao, et al. ESPRIT method for blade vibration frequency identification in rotating machinery based on Blade tip-timing measurement[J]. Journal of Vibration and Shock, 2010, 29(12):18-22.
[11] CHEN Z S, YANG Y M, XIE Y, et al., Non-contact crack detection of high-speed blades based on principal component analysis and Euclidian angles using optical-fiber sensors[J], Sensors & Actuators A Physical. 2013, 201(10):66-72.
[12] CHEN Z S, YANG Y M, GUO B, et al. Blade damage prognosis based on kernel principal component analysis and grey model using subsampled tip-timing signals[J]. Proceedings of the Institution of Mechanical Engineers-Part C:Journal of Mechanical Engineering Science, 2014, 228(17):3178-3185.
[13] HU Z, LIN J, CHEN Z S, et al. A non-uniformly under-sampled blade tip-timing signal reconstruction method for blade vibration monitoring[J]. Sensors, 2015, 15(2):2419-2437.
[14] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[15] 王丹. 航空发动机压气机叶片流固耦合振动的动力学特性研究[D]. 哈尔滨:哈尔滨工业大学, 2016. WANG Dan. Study on the dynamics of vibrations for the aero-engine compressor blades consided the fluid-structure interactions[D]. Harbin:Harbin Institute of Technology, 2016.
[16] 焦卫东,杨世锡,钱苏翔,等. 联合应用MUSIC与FastICA算法实现多个时空混叠源信号的波形重建[J]. 机械工程学报, 2010, 46(6):63-70. JIAO Weidong, YANG Shixi, QIAN Suxiang, et al. Waveform reconstruction of multiple spatio-temporal mixed sources by the joint use of MUSIC and fast ICA[J]. Journal of Mechanical Engineering, 2010, 46(6):63-70.

基于压缩感知的叶端定时欠采样多频叶片振动盲重构研究

Compressed Sensing-based Blind Reconstruction of Multi-frequency Blade Vibration from Under-sampled BTT Signals

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