稀疏度自适应匹配追踪的欠采样BTT信号重构方法

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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。

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	张继旺1
	唐雨2
	丁克勤1
	张旭1
	陈光1

关键词 ：叶尖定时, 欠采样, 稀疏重构；测量矩阵；稀疏度

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.

Key words： blade time timing under-sampled signal sparsity reconstruction；measurement matrix；sparsity

收稿日期: 2023-02-08 出版日期: 2023-10-28

引用本文:

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. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(20): 286-292.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I20/286

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