航空发动机转子非稳态振动分量提取方法
收稿日期: 2022-10-24
修回日期: 2023-03-20
录用日期: 2023-04-25
网络出版日期: 2023-04-28
基金资助
173重点基础研究项目
Extraction method for unsteady vibration components of aero-engine rotors
Received date: 2022-10-24
Revised date: 2023-03-20
Accepted date: 2023-04-25
Online published: 2023-04-28
Supported by
National Key Basic Research Project of 173
对发动机转子振动状态进行监测是提高发动机可靠性的重要方法之一。然而在实际运行与振动监测过程中发现，发动机转速变化快且转子振动分量微弱，其转子谐频振动分量难以实时跟踪提取。为解决这一问题，提出了基于稀疏谐波乘积谱及自适应Vold-Kalman滤波的航空发动机转子振动分量提取方法。首先，分析了发动机机匣信号特性，通过机匣信号中叶片通过频率与转频的谐波关系，结合谐波乘积谱的思路，提出了一种快速计算发动机转频的方法，该方法无需准确的键相信息。其次，使用变步长迭代的方法最小化阶次谱残余误差，确定Vold-Kalman滤波器的最佳滤波参数，从而实现发动机转子谐频振动分量的准确提取。通过仿真数据验证了本方法在较大的噪声下仍能够很好地提取出机匣信号中微弱的转子振动分量，并与多种经典信号分解方法进行了对比。最后，对实际发动机信号进一步分析，针对发动机启停、加减速状态2种典型非稳态工况进行了计算并验证，证明了所提方法的优越性。
关键词： 航空发动机; 谐波乘积谱; Vold-Kalman滤波; 非稳态振动; 转子
肖袁 , 冯坤 , 胡明辉 , 江志农 . 航空发动机转子非稳态振动分量提取方法[J]. 航空学报, 2024 , 45(3) : 228158 -228158 . DOI: 10.7527/S1000-6893.2023.28158
Monitoring the vibration state of engine rotors is one of the important methods to improve the reliability of aero-engines. However， in actual operation and vibration monitoring process， it is found that the engine speed changes rapidly， and the rotor vibration component is weak， which makes it difficult to track and extract the rotor harmonic vibration component in real time. To solve this problem， this paper proposes an extraction method for aero-engine rotor vibration components based on sparse harmonic product spectrum and adaptive Vold-Kalman filter. Firstly， the characteristics of the engine casing signal is analyzed. Through combining the harmonic relationship between blade passing frequency and rotation frequency with the idea of harmonic product spectrum， a method for calculating the engine rotation frequency rapidly is proposed， without the need for accurate phase information. Secondly， the residual error of the order spectrum is minimized by using the method of variable step iteration， and the optimal filtering parameters of the Vold-Kalman filter are determined， to realize the accurate extraction of the harmonic vibration component of the engine rotor. It is verified by simulation signals that this method can effectively extract the weak rotor vibration components from the casing signal under relatively large noise， and this method is compared with a variety of classical signal decomposition methods. Finally， actual engine signals are further analyzed， and the calculation and verification are carried out for two typical unsteady working conditions of engine start/stop and acceleration/deceleration， which proves the superiority of the proposed method.
| 1 | 宋兆泓. 航空发动机典型故障分析［M］. 北京： 北京航空航天大学出版社， 1993： 1-4. |
| ZHAOHONG S. Typical failure analysis of aero-engine［M］. Beijing： Beihang University Press， 1993： 1-4 （in Chinese）. | |
| 2 | 陈予恕， 张华彪. 航空发动机整机动力学研究进展与展望［J］. 航空学报， 2011， 32（8）： 1371-1391. |
| CHEN Y S， ZHANG H B. Review and prospect on the research of dynamics of complete aero-engine systems［J］. Acta Aeronautica et Astronautica Sinica， 2011， 32（8）： 1371-1391 （in Chinese）. | |
| 3 | 徐可君， 秦海勤， 江龙平. 基于EMD和HHT的航空发动机转子-机匣振动信号分析［J］. 振动与冲击， 2011， 30（7）： 237-240. |
| XU K J， QIN H Q， JIANG L P. Rotor-case vibration signal analysis of an aero engine based on EMD and HHT［J］. Journal of Vibration and Shock， 2011， 30（7）： 237-240 （in Chinese）. | |
| 4 | YAN R Q， GAO R X， CHEN X F. Wavelets for fault diagnosis of rotary machines： A review with applications［J］. Signal Processing， 2014， 96： 1-15. |
| 5 | 殷勤业， 黄朝云. 时－频分析中的不确定性原理［J］. 西安交通大学学报， 1996， 30（9）： 1-7. |
| YINQIN Y， HUANG Z Y. Concept of the uncertainty principle in time and frequency analysis［J］. Journal of Xi’an Jiaotong University， 1996， 30（9）： 1-7 （in Chinese）. | |
| 6 | DAUBECHIES I， LU J F， WU H T. Synchrosqueezed wavelet transforms： An empirical mode decomposition-like tool［J］. Applied and Computational Harmonic Analysis， 2011， 30（2）： 243-261. |
| 7 | YU G， YU M J， XU C Y. Synchroextracting transform［J］. IEEE Transactions on Industrial Electronics， 2017， 64（10）： 8042-8054. |
| 8 | LEI Y G， LIN J， HE Z J， et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery［J］. Mechanical Systems and Signal Processing， 2013， 35（1-2）： 108-126. |
| 9 | LEI Y G， HE Z J， ZI Y Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery［J］. Mechanical Systems and Signal Processing， 2009， 23（4）： 1327-1338. |
| 10 | ZHAO X M， PATEL T H， ZUO M J. Multivariate EMD and full spectrum based condition monitoring for rotating machinery［J］. Mechanical Systems and Signal Processing， 2012， 27： 712-728. |
| 11 | FELDMAN M. Analytical basics of the EMD： Two harmonics decomposition［J］. Mechanical Systems and Signal Processing， 2009， 23（7）： 2059-2071. |
| 12 | VOLD H， LEURIDAN J. High resolution order tracking at extreme slew rates， using Kalman tracking filters［C］∥SAE Technical Paper Series. Warrendale： SAE International， 1993： 931288. |
| 13 | PAN M C， CHU W C， LE D D. Adaptive angular-velocity Vold-Kalman filter order tracking - Theoretical basis， numerical implementation and parameter investigation［J］. Mechanical Systems and Signal Processing， 2016， 81： 148-161. |
| 14 | FORBES G L， RANDALL R B. Estimation of turbine blade natural frequencies from casing pressure and vibration measurements［J］. Mechanical Systems and Signal Processing， 2013， 36（2）： 549-561. |
| 15 | SRIPRIYA N， NAGARAJAN T. Pitch estimation using harmonic product spectrum derived from DCT［C］∥ 2013 IEEE International Conference of IEEE Region 10 （TENCON 2013）. Piscataway： IEEE Press， 2014. |
| 16 | PATIL A P， MISHRA B K， HARSHA S P. Fault diagnosis of rolling element bearing using autonomous harmonic product spectrum method［J］. Proceedings of the Institution of Mechanical Engineers， Part K： Journal of Multi-Body Dynamics， 2021， 235（3）： 396-411. |
| 17 | ZHAO M， LIN J， MIAO Y H， et al. Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings［J］. Measurement， 2016， 91： 421-439. |
| 18 | KIELB R E， BARTER J W， THOMAS J P， et al. Blade excitation by aerodynamic instabilities： A compressor blade study［C］∥Proceedings of ASME Turbo Expo 2003， Collocated With the 2003 International Joint Power Generation Conference. NewYork： ASME， 2009： 399-406. |
| 19 | FENG Z P， ZHU W Y， ZHANG D. Time-Frequency demodulation analysis via Vold-Kalman filter for wind turbine planetary gearbox fault diagnosis under nonstationary speeds［J］. Mechanical Systems and Signal Processing， 2019， 128： 93-109. |
| 20 | LI Y B， FENG K， LIANG X H， et al. A fault diagnosis method for planetary gearboxes under non-stationary working conditions using improved Vold-Kalman filter and multi-scale sample entropy［J］. Journal of Sound and Vibration， 2019， 439： 271-286. |
| 21 | ZHAO D Z， CHENG W D， GAO R X， et al. Generalized vold-kalman filtering for nonstationary compound faults feature extraction of bearing and gear［J］. IEEE Transactions on Instrumentation and Measurement， 2020， 69（2）： 401-410. |
| 22 | VOLD H， LEURIDAN J. High resolution order tracking at extreme slew rates using Kalman tracking filters［J］. Shock and Vibration， 1995， 2（6）： 507-515. |
| 23 | 张文海. 多转子燃气轮机谐频振动同步跟踪方法研究［D］. 北京： 北京化工大学， 2020： 44-45. |
| ZHANG W H. Research on harmonic frequency synchronous tracking method of multi-rotor gas turbine［D］. Beijing： Beijing University of Chemical Technology， 2020： 44-45 （in Chinese）. |
/
| 〈 |
|
〉 |
CJA