摘要
在机械设备故障诊断系统中应用压缩感知(CS)可以有效缓解故障诊断系统数据的传输和存储压力。将观测矩阵的优化设计方法引入机械设备故障诊断系统中。结合机械信号信噪比(SNR)较低的特点,在分析不同观测矩阵优化框架抗噪性能的基础上,得出适用于机械信号的鲁棒性观测矩阵优化框架。基于该优化框架,推导出一种比现有求解方法计算复杂度更低的解析解,提高了优化观测矩阵的求解速度。数值仿真和实验结果表明,所提方法得到的优化观测矩阵具有良好的鲁棒性和更高的计算效率,相比现有的优化观测矩阵和常用的随机矩阵,所提方法可以在更低的信噪比和压缩比下有效地重构机械故障信号。
The application of compressed sensing(CS)in mechanical equipment fault diagnosis system can effectively alleviate the pressure of data transmission and storage in fault diagnosis system.The optimal design method of sensing matrix is introduced into mechanical fault diagnosis system for the first time.Considering the characteristics of low signal-to-noise ratio(SNR)of mechani⁃cal signals,a robust sensing matrix optimization framework suitable for mechanical signals is proposed based on the analysis of the robustness of different optimization frameworks of sensing matrix.A new closed-form algorithm with lower computational complex⁃ity is derived for the proposed optimization framework.Numerical simulations and experiments are carried out and the results show that the optimal sensing matrix obtained by the proposed method is robust and computationally efficient.Compared with the exist⁃ing optimal sensing matrix and the commonly used random matrix,the proposed method can effectively reconstruct the mechanical fault signals at lower signal-to-noise ratio and compression ratio.
作者
林慧斌
陈伟良
LIN Hui-bin;CHEN Wei-iang(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2023年第1期288-298,共11页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(51875207)
广东省基础与应用基础研究基金资助项目(2022A1515011238)。
关键词
故障诊断
轴承
压缩感知
观测矩阵优化设计
鲁棒性
fault diagnosis
bearing
compressed sensing
optimal design of sensing matrix
robustness
