叶端定时（blade tip timing, BTT）技术是当前研究重大装备动叶片状态监测与故障诊断的趋势，但BTT技术固有的非均匀、欠采样特性诱使动叶片振动参数辨识困难。本文围绕动叶片异步振动参数辨识问题，首先，通过快速傅里叶变换（fast Fourier transform, FFT）算法提取动叶片异步振动幅值和异步振动频率的非整数阶次；随后，改进现有多信号分类（multiple signal classification, MUSIC）算法，提出基于阶次搜索MUSIC（engine order search-based multiple signal classification, EOS-MUSIC）的动叶片异步振动频率整数阶次搜索策略；最后，融合EOS-MUSIC算法与FFT算法分析结果辨识动叶片异步振动参数。基于MATLAB软件仿真动叶片异步振动信号，与现有MUSIC算法比较，验证了EOS-MUSIC算法的可信性和准确性。在离心压气机试验台开展叶轮叶片振动试验，与应变片法相比，EOS-MUSIC算法频率辨识绝对误差为3.36Hz，相对误差仅为0.53%。本文在FFT算法预处理的基础上，通过阶次搜索辨识动叶片异步振动参数，克服了现有MUSIC算法搜索周期长和辨识精度低的难题，为动叶片异步振动参数辨识提供了理论支撑。
Abstract
Blade tip timing (BTT) technology is currently the trend in condition monitoring and fault diagnosis of rotating blades in major equipment. Nonetheless, the BTT technique, with characteristics of non-uniformity and under-sampling, presents challenges in identifying the blade vibration parameters. To address the issue of asynchronous vibration parameter identification in rotating blades, this paper initially utilizes the fast Fourier transform (FFT) algorithm to extract the non-integer engine order and the amplitude of blade asynchronous vibration frequency. Subsequently, an improved multiple signal classification (MUSIC) algorithm is employed to propose an integer engine order search strategy for blade asynchronous vibration frequency based on MUSIC algorithm (EOS-MUSIC). Finally, this study proposes an asynchronous vibration parameter identification algorithm of rotating blades based on the EOS-MUSIC algorithm and the FFT algorithm. The MATLAB software was utilized for simulating the signals of blade asynchronous vibration, and the feasibility and reliability of the proposed algorithm were validated by comparison with the existing MUSIC algorithm. Experiments on impeller blade vibration were conducted on a large-scale centrifugal compressor test rig. The absolute error of the frequency identification was 3.36Hz, and the relative error was only 0.53% compared with the results of the strain gauge method. Based on the preprocessing of FFT algorithm, this paper extracts the blade asynchronous vibration parameters by engine order search, which overcomes the problems of long calculation period and severe identification errors of the existing MUSIC algorithm. This study provides theoretical support for the asynchronous vibration parameters identification of rotating blades.
关键词
叶端定时 /
异步振动 /
阶次搜索 /
参数辨识
{{custom_keyword}} /
Key words
blade tip timing /
blade asynchronous vibration /
engine order search /
parameters identification
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 陈艳平. 高速旋转叶片的非线性动力学研究[D]. 北京: 北京工业大学, 2012.
[2] Li Jing, Aye-Addo N, Kielb R, et al. Mistuned higher-order mode forced response of an embedded compressor rotor, Part I: steady and unsteady aerodynamics[J]. Journal of Turbomachinery, 2018, 140(3): 1-13.
[3] 李彦, 都建京, 陈星, 等. 高压压气机转子叶片断裂分析[J]. 失效分析与预防, 2019, 14(3): 188-192.
Li Y, Du J J, Chen X, et al. Failure analysis of rotor blades in high-pressure compressor[J]. Failure Analysis and Prevention, 2019, 14(3): 188-192.
[4] Russhard P. The rise and fall of the rotor blade strain gauge[C]//Vibration Engineering and Technology of Machinery, England, 2015: 27-37.
[5] Chen Z S, Sheng H, Xia Y M, et al. A comprehensive review on blade tip timing-based health monitoring: status and future[J]. Mechanical Systems and Signal Processing, 2021, 149: 107330.
[6] 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信号重建[J]. 机械工程学报, 2011, 47(13): 98-103.
Li M L, Duan F J, Ouyang T, et al. Reconstruction of the blade vibration signal from rotating machinery based on blade tip-timing measurement[J]. Journal of Mechanical Engineering, 2011, 47(13): 98-103.
[7] 欧阳涛, 段发阶, 李孟麟, 等. 旋转叶片异步振动全相位FFT辨识方法[J]. 振动工程学报, 2011, 24(3): 268-273.
Ouyang T, Duan F J, Li M L, et al. A method for identifying rotating blade asynchronous vibration by all-phase FFT[J]. Journal of Vibration Engineering, 2011, 24(3): 268-273.
[8] Vercoutter A, Lardies J, Berthillier M, et al. Improvement of compressor blade vibrations spectral analysis from tip timing data: Aliasing reduction[C]//Turbo Expo: Power for Land, Sea, and Air. American Society of Mechanical Engineers, San Antonio, USA, 2017: V07AT26A006.
[9] Carassale L, Coletti F, Guida R, et al. Multi-channel spectral analysis of non-synchronous vibrations of bladed disks measured by blade tip timing[C]//Turbo Expo: Power for Land, Sea, and Air. American Society of Mechanical Engineers, Online, 2020: V011T30A033.
[10] Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[11] Chen S Y, Yang Y M, Hu H F, et al. Compressed sensing-based order analysis for blade tip timing signals measured at varying rotational speed[J]. Shock and Vibration, 2020: 1-12.
[12] Chen Z S, Sheng H, Xia Y M. Multi-coset angular sampling-based compressed sensing of blade tip-timing vibration signals under variable speeds[J]. Chinese Journal of Aeronautics, 2021, 34(9): 83-93.
[13] Dong J N, Li H K, Fan Z F, et al. Time-frequency sparse reconstruction of non-uniform sampling for non-stationary signal[J]. IEEE Transactions on Vehicular Technology, 2021, 70(11): 11145-11153.
[14] Dong J N, Li H K, Fan Z F, et al. A joint optimization algorithm using adaptive minimum coset number based discrete multi-coset sampling[J]. IEEE Access, 2020, 8: 168659-168670.
[15] 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+235.
Li M L, Duan F J, Ouyang T, 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): 18-21+235.
[16] 贺长波, 李宏坤, 赵新维, 等. 基于总体最小二乘准则旋转不变子空间法的叶尖定时欠采样信号分析[J]. 机械工程学报, 2019, 55(19): 103-111.
He C B, Li H K, Zhao X W, et al. Analysis method for under-sampled blade tip-timing signal based on the rotational invariance technique with total least squares principle[J]. Journal of Mechanical Engineering, 2019, 55(19): 103-111.
[17] Wang Z K, Yang Z B, Wu S M, et al. An improved multiple signal classification for nonuniform sampling in blade tip timing[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(10): 7941-7952.
[18] Wang Z K, Yang Z B, Li H Q, et al. Subspace dimension reduction for faster multiple signal classification in blade tip timing[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70.
[19] Liu Z B, Duan F J, Niu G Y, et al. Reconstruction of blade tip-timing signals based on the MUSIC algorithm[J]. Mechanical Systems and Signal Processing, 2022, 163: 108137.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(1993 KB)
