基于叶端定时的压气机喘振特征频域辨识

平艳; 王增坤; 范志飞; 袁超; 杨志勃; 乔百杰

doi:10.13224/j.cnki.jasp.20240476

基于叶端定时的压气机喘振特征频域辨识

doi: 10.13224/j.cnki.jasp.20240476

平艳^1,,
王增坤^{2, 3,*, ,},
范志飞¹,
袁超¹,
杨志勃³,
乔百杰³

1.
中国东方电气集团有限公司东方汽轮机有限公司，四川德阳 618000
2.
长安大学道路施工技术与装备教育部重点实验室，西安 710064
3.
西安交通大学航空动力系统与等离子体技术全国重点实验室，西安 710049

基金项目: 国家自然科学基金（52405088，52075414，92360306）；国家资助博士后研究人员计划（GZC20241446）

详细信息

作者简介:
平艳（1986-），女，高级工程师，硕士，主要研究方向为动力装备方案设计。E-mail：pingyan@dongfang.com

通讯作者:
王增坤（1996-），男，讲师，博士，主要研究方向为转子叶片非接触状态监测。E-mail：zengkunwang@163.com

中图分类号: V231.92
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出版历程
- 收稿日期: 2024-07-15
- 网络出版日期: 2024-11-23

Identification of compressor surge based on blade tip timing in frequency domain

PING Yan^1
,,
WANG Zengkun^{2, 3,*
, ,},
FAN Zhifei¹,
YUAN Chao¹,
YANG Zhibo³,
QIAO Baijie³

1.
Dongfang Turbine Company Limited，Dongfang Electric Corporation，Deyang Sichuan 618000，China
2.
Key Laboratory of Road Construction Technology and Equipment of MOE，Chang’an University，Xi’an 710064，China
3.
National Key Lab of Aerospace Power System and Plasma Technology，Xi’ an Jiaotong University，Xi’an 710049，China

摘要

摘要:
提出了一种基于转子叶片叶端定时（BTT）信号的幅频多重信号分类（MUSIC）法，用于进行压气机喘振特征辨识。通过理论分析转子叶片叶端定时的实正弦振动信号模型，挖掘异步振动的幅值和频率与自相关矩阵特征值和特征向量的对应关系，实现了MUSIC法框架内的伪谱幅值补偿。开展压气机喘振的叶端定时实验，通过与快速傅里叶变换、最小二乘拟合方法的喘振特征辨识效果进行对比，对所提方法进行了验证。结果表明：喘振故障在时域对应显著增大的振动位移和转速的波动，在频域对应大幅值的低频异步振动特征。所提方法相比最小二乘拟合法和傅里叶变换，能够准确辨识5.3 Hz的喘振特征频率，并且其幅值聚焦性超过傅里叶变换的3倍，具有更高的频率分辨率和幅值识别精度，能够有效稳定地提取喘振故障的频域特征。
- 压气机叶片 /
- 叶端定时（BTT） /
- 喘振 /
- 多重信号分类（MUSIC） /
- 频域分析
Abstract:
An amplitude-identified multiple signal classification (MUSIC) algorithm for blade tip timing (BTT) signals of rotor blades was proposed for identifying surge characteristics of compressors. By analyzing the real-valued sinusoidal signal model of BTT, the relationship between the amplitude and frequency of asynchronous vibrations and the eigenvalues and eigenvectors of the autocorrelation matrix was explored, allowing for compensation of the amplitude of the pseudo-spectrum within the framework of the MUSIC algorithm. The experiment of rotor surge identification using BTT technique was conducted. By comparing with fast Fourier transform and the least squares fitting method, the effectiveness of proposed method was verified. The results indicated that surge faults exhibited a significant increase in vibration displacement and speed fluctuation in the time domain. In the frequency domain, surge faults exhibited low-frequency asynchronous vibration with large amplitude. Compared with Fourier transform and least squares fitting, the proposed method can accurately identify the surge characteristic frequency of 5.3 Hz, and the focusing capability of its amplitude identification was more than three times that of the Fourier transform. The proposed method with higher frequency resolution and amplitude recognition accuracy can effectively extract the characteristics of surge faults in the frequency domain.
- compressor blade /
- blade tip timing (BTT) /
- surge /
- multiple signal classification (MUSIC) /
- frequency analysis

HTML

图 1 叶端定时测量原理示意图

Figure 1. Measurement principle of blade tip timing

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图 2 同步振动叶端定时采样信号示例

Figure 2. Example of synchronous vibration sampled by blade tip timing

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图 3 异步振动叶端定时采样信号示例

Figure 3. Example of asynchronous vibration sampled by blade tip timing

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图 4 喘振实验叶端定时传感器安装示意图

Figure 4. Placement of blade tip timing sensors for the surge test

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图 5 叶端定时实验信号时域图

Figure 5. Experimental signal of BTT in the time domain

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图 6 1号叶片叶端定时实验信号FFT频域分析结果

Figure 6. FFT results of the experimental signal of BTT signal of blade 1 in the frequency domain

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图 7 1号叶片叶端定时实验信号LSF频域分析结果

Figure 7. LSF results of the experimental signal of BTT signal of blade 1 in the frequency domain

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图 8 1号叶片叶端定时实验信号幅频MUSIC频域分析结果

Figure 8. Amplitude-identified MUSIC results of the experimental signal of BTT signal of blade 1 in the frequency domain

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图 9 2号叶片叶端定时实验信号FFT频域分析结果

Figure 9. FFT results of the experimental signal of BTT signal of blade 2 in the frequency domain

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图 10 2号叶片叶端定时实验信号LSF频域分析结果

Figure 10. LSF results of the experimental signal of BTT signal of blade 2 in the frequency domain

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图 11 2号叶片叶端定时实验信号幅频MUSIC频域分析结果

Figure 11. Amplitude-identified MUSIC results of the experimental signal of BTT signal of blade 2 in the frequency domain

下载: 全尺寸图片幻灯片

表 1 1号叶片喘振特征辨识结果

Table 1. Identified surge features of blade 1

参数方法
FFT LSF 幅频MUSIC

频率/Hz 5.35 5.3 5.3
幅值/μm 68.7 7.21 224.5
发生时刻/s 79.2504 79.2504 79.2504

下载: 导出CSV

表 2 2号叶片喘振特征辨识结果

Table 2. Identified surge features of blade 2

参数方法
FFT LSF 幅频MUSIC

频率/Hz 5.35 5.3 5.3
幅值/μm 68.9 7.07 226.8
发生时刻/s 79.2504 79.2504 79.2504

下载: 导出CSV

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