Characteristics analysis of blade tip timing signals in synchronous resonance and frequency recovery based on subspace pursuit algorithm
Characteristics analysis of blade tip timing signals in synchronous resonance and frequency recovery based on subspace pursuit algorithm
复制标题
基于子空间追踪算法的叶尖正时信号同步谐振及频率恢复特性分析
DOI：
10.1016/j.ymssp.2022.109632
复制
发表时间：
2022
影响因子：
8.4
通讯作者：
Chen Yugang
中科院分区：
文献类型：
--
作者：
Dong Jiannan;Li Hongkun;Fan Zhenfang;Zhao Xinwei;Wei Daitong;Chen Yugang
长期高周疲劳振动会导致叶片寿命降低。叶尖正时（BTT）技术是叶片非接触测量领域的研究热点。由于传感器数量的限制，BTT信号存在严重的欠采样情况。通过分析欠采样信号来识别叶片振动频率是当前BTT技术的关键问题。可以应用适用于BTT采样模型的称为多陪集采样(MCS)的亚奈奎斯特技术。本文描述了叶片同步共振下BTT信号的特性，并指出了该特性在正交匹配追踪(OMP)算法恢复过程中的不足。针对该问题，提出了适应该特点的子空间追踪（SP）算法。通过叶片旋转实验验证了算法的有效性。该算法的优点是能够以较高的概率恢复叶片的同步谐振频率，为BTT技术的发展提供了新的思路。
Long-term high cycle fatigue vibration will lead to the reduction of life of the blade. Blade Tip Timing (BTT) technology is a research hotspot in the non-contact measurement of blades. Due to the limitation of the number of sensors, BTT signals present serious undersampled conditions. Identifying the frequency of blade vibration through the analysis of undersampled signals is the key problem of the current BTT technology. A sub-Nyquist technique, called multi-coset sampling (MCS), which is suitable for the BTT sampling model can be applied. In this paper, the charac-teristics of BTT signals under blade synchronous resonance are described and the deficiency of this feature in the recovery process using the orthogonal matching pursuit (OMP) algorithm is found. Based on the problem, the subspace pursuit (SP) algorithm adapted to this feature is proposed. The validity of the algorithm is verified by the blade rotation experiment. The advantage of this algorithm is that the synchronous resonance frequency of the blades can be recovered with a higher probability, which provides a new idea for the development of BTT technology.
登录
查看更多内容
DOI：
--
发表时间：
2008
期刊：
ArXiv
影响因子：
--
作者：
Wei Dai;O. Milenkovic
通讯作者：
Wei Dai;O. Milenkovic
影响因子：
14.3
作者：
Patrick Spettel;H. Beyer;Michael Hellwig
通讯作者：
Patrick Spettel;H. Beyer;Michael Hellwig
影响因子：
3.9
作者：
Li Haifeng;Wen Jinming
通讯作者：
Wen Jinming
叶尖正时传感器布置的物理约束融合等角紧框架法
影响因子：
5.6
作者：
Wu Shuming;Zhao Zhibin;Yang Zhibo;Tian Shaohua;Yang Laihao;Chen Xuefeng
通讯作者：
Chen Xuefeng
影响因子：
8.4
作者：
Jinghui Xu;Baijie Qiao;Junjian Liu;Chunyan Ao;Guangrong Teng;Xuefeng Chen
通讯作者：
Jinghui Xu;Baijie Qiao;Junjian Liu;Chunyan Ao;Guangrong Teng;Xuefeng Chen