摘要
针对传统基于香农-奈奎斯特理论的机械故障检测方法信号采样率要求高、故障检测率低的不足,提出一种基于压缩感知的机械故障信号检测方法。利用压缩感知基础理论构建随机高斯测量矩阵,对降噪后的原始故障信号压缩降维,并采用范数稀疏逼近法求出压缩感知矩阵的稀疏解;提取原始压缩故障信号中的时域特征和能量特征,实现对原始故障信号的重构。仿真结果表明:提出的故障检测方法的信号重构效果更好,故障检测率可达96.16%。
Aiming at the shortcomings of traditional mechanical fault detection methods based on Shannon-Nyquist theory,such as high sampling rate and low fault detection rate,a mechanical fault signal detection method based on compressed sensing was pro⁃posed.The random Gaussian measurement matrix was constructed based on the basic theory of compressed sensing,and the dimension of the original fault signal was reduced by compression after noise reduction.The sparse solution of the compressed sensing matrix was obtained by the norm sparse approximation method.The time domain and energy features of the original compressed fault signal were ex⁃tracted to reconstruct the original fault signal.The simulation results show that the signal reconstruction effect of the proposed fault de⁃tection method is better,and the detection rate of the fault signal can reach 96.16%.
作者
鹿洪荣
LU Hongrong(Shandong Polytechnic,Jinan Shandong 250104,China)
出处
《机床与液压》
北大核心
2021年第4期183-188,共6页
Machine Tool & Hydraulics
关键词
故障信号检测
压缩感知
高斯测量矩阵
Fault signal detection
Compressed sensing
Gauss measurement matrix
