Compressed sensing with nonconvex sparse regularization and convex analysis for duct mode detection
Compressed sensing with nonconvex sparse regularization and convex analysis for duct mode detection
复制标题
用于管道模式检测的非凸稀疏正则化和凸分析的压缩感知
DOI：
10.1016/j.ymssp.2020.106930
复制
发表时间：
2020-11
影响因子：
8.4
通讯作者：
Dong Guangming
中科院分区：
文献类型：
--
作者：
Hou Fatao;Chen Jin;Dong Guangming
近年来，基于压缩感知的模式检测方法越来越受到人们的关注，因为它可以减少经典的Shannon-Nyquist采样理论所需的传感器数量。提出了一种用于航空发动机风扇噪声方位模态检测的非凸稀疏正则化方法。非凸稀疏正则化基于广义极小极大凹（GMC）惩罚，可以保持稀疏正则化最小二乘代价函数的凸性，从而可以用凸优化算法求解全局最优解。在模态检测方面，GMC方法相对于传统压缩感知方法的主要优点是，GMC方法在传感器数量较少的情况下可以更好地恢复模态幅度。此外，GMC方法可以有效地抑制背景噪声或传感器安装误差引起的不相关模式。因此，所提出的管道模态检测方法可以显著提高检测模态的准确性。数值仿真和实验验证了GMC方法在航空发动机风扇噪声模态检测中的有效性，对比研究表明，GMC方法在压缩感知和传统模态检测方法中提供了比1.1最小化更准确的模态检测结果。
Recently, research interests are increasing in mode detection methods based on compressed sensing, as it can reduce the number of sensors required by the classical Shannon-Nyquist sampling theory. This paper proposes a nonconvex sparse regularization method for azimuthal mode detection for aero engine fan noise. The nonconvex sparse regularization is based on the generalized minimax-concave (GMC) penalty, which can maintain the convexity of the sparse-regularized least squares cost function, and thus the global optimal solution can be solved by convex optimization algorithms. The main advantage of the GMC method over conventional compressed sensing method in mode detection is that the GMC method can better recover the mode amplitudes with a small number of sensors. Besides, the GMC method can suppress effectively the irrelated modes induced by the background noise or sensor installation errors. Therefore, the proposed method for duct mode detection can significantly improve the accuracy of the detected modes. Numerical simulations and experimental tests verify the effectiveness of the GMC method in mode detection for aero engine fan noise, and comparison studies show that the GMC method provides more accurate mode detection results than l 1 minimization in the category of compressed sensing, as well as traditional mode detection methods.
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影响因子：
2.2
作者：
D. Blacodon;S. Lewy
通讯作者：
D. Blacodon;S. Lewy
影响因子：
5.4
作者：
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通讯作者：
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DOI：
10.1111/j.1467-9868.2011.00771.x
发表时间：
2011-01-01
影响因子：
5.8
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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轴承故障诊断的非凸稀疏正则化和凸优化
影响因子：
7.7
作者：
Shibin Wang;Ivan Selesnick;Gaigai Cai;Yining Feng;Xin Sui;Xuefeng Chen
通讯作者：
Xuefeng Chen