稀疏度自适应匹配追踪的欠采样BTT信号重构方法

张继旺1,唐雨2,丁克勤1,张旭1,陈光1

振动与冲击 ›› 2023, Vol. 42 ›› Issue (20) : 286-292.

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (20) : 286-292.
论文

稀疏度自适应匹配追踪的欠采样BTT信号重构方法

  • 张继旺1,唐雨2,丁克勤1,张旭1,陈光1
作者信息 +

Reconstruction of the blade tip-timing based on a modified sparsity adaptive matching pursuit algorithm

  • ZHANG Jiwang1, TANG Yu2, DING Keqin1, ZHANG Xu1, CHEN Guang1
Author information +
文章历史 +

摘要

为了解决叶尖定时技术所测量信号严重的欠采样问题,引入了稀疏重构算法,基于叶尖定时技术采样原理构建了用于信号重构的测量矩阵,并利用稀疏度匹配追踪原则对传统稀疏重构算法需要预先确定稀疏度的前提条件进行了改进,克服了传统稀疏重构算法的不适用问题。利用改进算法只需少量的欠采样数据就可以重构出完整的旋转叶片振动信号,即所重构出的信号能够满足采样定理的要求。最后为了验证该方法的可行性和准确性,采用数值建模与实验测试进行了对比分析,结果显示所提方法所重构的信号与仿真建模信号的频率成分误差小于0.15%,表明该方法具有良好的重构效果。

Abstract

In order to solve the serious under-sampled problem of the signals measured by the tip timing technology, the sparse reconstruction algorithm is introduced, and the measurement matrix for signal reconstruction is constructed based on the sampling principle of the tip timing technology, and the condition that the traditional sparse reconstruction algorithm needs to determine the sparsity in advance is improved by using the sparsity matching tracking principle, which overcomes the inapplicability of the traditional sparse reconstruction algorithm. Using the improved algorithm, only a small amount of under-sampled data can be used to reconstruct the complete rotating blade vibration signal, that is, the reconstructed signal can meet the requirements of the sampling theorem. Finally, in order to verify the feasibility and accuracy of this method, numerical modeling and experimental testing are used to make a comparative analysis. The results show that the frequency component error between the reconstructed signal and the simulated modeling signal is less than 0.15%, indicating that the method has a good reconstruction effect.

关键词

叶尖定时 / 欠采样 / 稀疏重构;测量矩阵;稀疏度

Key words

blade time timing / under-sampled signal / sparsity reconstruction;measurement matrix;sparsity

引用本文

导出引用
张继旺1,唐雨2,丁克勤1,张旭1,陈光1. 稀疏度自适应匹配追踪的欠采样BTT信号重构方法[J]. 振动与冲击, 2023, 42(20): 286-292
ZHANG Jiwang1, TANG Yu2, DING Keqin1, ZHANG Xu1, CHEN Guang1. Reconstruction of the blade tip-timing based on a modified sparsity adaptive matching pursuit algorithm[J]. Journal of Vibration and Shock, 2023, 42(20): 286-292

参考文献

[1] 王维民,张旭龙,陈康,等. 大型轴流压气机叶片无键相振动监测与故障预警方法[J].振动与冲击,2019,38(23): 54-61+118.
WANG Weimin,ZHANG Xulong,CHEN Kang,et al. Vibration monitoring and fault pre-warning method for large axial compressor blades without OPR sensor[J]. JOURNAL OF VIBRATION AND SHOCK,2019,38(23): 54-61+118.
[2] 王维民,陈子文,张旭龙,等. 基于叶端定时的转子碰摩故障诊断方法[J]. 航空学报,2022,43(08): 36-45.
Wang Weimin,CHEN Ziwen,ZHANG Xulong,et al. Fault diagnosis method of rotor rub impact based on blade tip timing[J]. Acta Aeronautica et Astronautica Sinica,2022,43(08): 36-45.
[3] 张继旺,丁克勤. 基于EDFT的非均匀欠采样叶尖定时信号分析[J]. 振动与冲击,2021,40(05): 39-45.
ZHANG Jiwang,DING Keqin. Analysis of Nonuniform and Under-sampling Tip Timing Signal Based on Extended DFT[J]. JOURNAL OF VIBRATION AND SHOCK,2021,40(05): 39-45.
[4] Giovanni Rigosi, Giuseppe Battiato, Teresa M. Berruti. Synchronous vibration parameters identification by tip timing measurements [J]. Mechanics Research Communications, 2017,79: 7-14.
[5] 张继旺,王雪莉,谢海博,等. 基于CNN的旋转叶片缺陷诊断方法[J]. 油气储运,2020,39(12): 1367-1372.
ZHANG Jiwang,WANG Xueli,XIE Haibo,et al. CNN-based defect diagnosis method for rotating blades[J]. Oil & Gas Storage and Transportation,2020,39(12): 1367-1372.
[6] 范博楠,张玉波,王海斗,等.基于叶尖定时技术的叶轮叶片动态监测研究现状[J]. 振动与冲击,2016,35(5): 96-102.
FAN Bo-nan,ZHANG Yu-bo,WANG Hai-dou,et al. Research status for dynamic monitoring impellers' of blades based on blade tip-timing[J]. JOURNAL OF VIBRATION AND SHOCK,2016,35(5): 96-102.
[7] 张亮,王启迪,李欣,等. 叶尖定时及叶尖间隙测量技术研究综述[J].河北科技大学学报,2021,42(05):431-441.
ZHANG Liang,WANG Qidi,LI xin,et al. Review of research on tip-timing and tip clearance measurement technology[J]. Journal of Hebei University of Science and Technology,2021,42(05):431-441.
[8] 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 Menglin,DUAN Fajie,OUYANG Tao,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.
[9] Qinglei Zhang, Dong Liu, Jianguo Duan, et al. Research on the identification of asynchronous vibration parameters of rotating blades based on blade tip timing vibration measurement theory [J]. Measurement, 2023, 206:1-13.
[10] Jun Lin, Zheng Hu, Zhong-Sheng Chen, 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.
[11]  Donoho DL. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[12] CANDÈS E, ROMBERG J, TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information [J]. IEEE Trans. on Information Theory, 2006, 52(2): 489-509.
[13] Anurag Singh,Samarendra Dandapat,Exploiting multi-scale signal information in joint compressed sensing recovery of multi-channel ECG signals,Biomedical Signal Processing and Control,2016,29: 53-66.
[14] Satheesh K. Perepu, Arun K. Tangirala, Reconstruction of missing data using compressed sensing techniques with adaptive dictionary, Journal of Process Control, 2016, 47: 175-190.
[15] Zhuo-Xu Cui,Qibin Fan,A nonconvex nonsmooth regularization method for compressed sensing and low rank matrix completion,Digital Signal Processing,2017,62: 101-111.
[16]  张效溥,田杰,孙宗翰,等.基于任意传感器排布的叶尖定时信号压缩感知辨识方法[J].航空动力学报,2020,35(01):41-51.
ZHANG Xiaopu,TIAN Jie,SUN Zonghan,et al.    Compressive sensing identification method of blade tip timing signals based on arbitrary sensor arrangement[J]. Journal of Aerospace Power,2020,35(01):41-51.

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