基于改进高斯随机测量矩阵的切削力信号压缩感知方法

高速加工过程中,依据传统Nyquist-Shannon采样定理进行信号采集通常会面临海量数据的存储、传输和处理难题.基于压缩感知理论提出了一种切削力信号采集新方法,实现信号压缩式采集.选择高斯随机矩阵作为基础测量矩阵,并结合近似正交三角分解和最小相关系数法对高斯随机矩阵进行重新设计,提高其压缩测量性能,再借助高效的压缩...

Compressed Sensing Method for Cutting Force Signals Based on Improved Gauss Random Measurement Matrix

In high-speed cutting processes, traditional Nyquist-Shannon sampling theorem was used for data collection which confront difficult problems of storage, transmission and processing for large amount of cutting force signals. A novel method of cutting force signal acquisition was proposed to realize the compression acquisition of signals based on the ompressed sensing theory. Gauss random matrix was selected as the basic measurement matrix and was redesigned by combining the approximate orthogonal upper triangular decomposition and the minimum correlation coefficient method to improve the compression measurement performance. Then the original cutting force signals were reconstructed from the measurement values by using the efficient compressive sampling matching pursuit algorithm. The experimental results show that the improved Gauss random measurement matrix has higher reconstruction accuracy and stability, and the proposed method greatly reduce the amount of data while ensuring the reconstruction efficiency and accuracy of cutting forces.