China Mechanical Engineering ›› 2021, Vol. 32 ›› Issue (18): 2231-2238.

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

WU Fenghe1,2;ZHANG  Ning1;LI Yuanxiang1;ZHANG Huilong1;GUO Baosu1,2

1. 1.College of Mechanical Engineering,Yanshan University,Qinhuangdao,Hebei,066004
2.Hebei Heavy-duty Intelligent Manufacturing Equipment Technology Innovation Center,Qinhuangdao,Hebei,066004
• Online:2021-09-25 Published:2021-10-14

### 基于改进高斯随机测量矩阵的切削力信号压缩感知方法

1. 1.燕山大学机械工程学院，秦皇岛，066004
2.河北省重型智能制造装备技术创新中心，秦皇岛，066004
• 通讯作者: 郭保苏（通信作者），男，1986年生，讲师。研究方向为智能排样、人工智能。发表论文20余篇。E-mail:guobaosu@ysu.edu.cn。
• 作者简介:吴凤和，男，1968年生，教授、博士研究生导师。研究方向为智能感知与数字孪生、智能制造。发表论文80余篇。E-mail:risingwu@ysu.edu.cn。
• 基金资助:
国家重点研发计划（2016YFC0802900）；
国家自然科学基金（51605422）；
河北省自然科学基金（E2017203372,E2017203156）；
河北省高等学校科学技术研究重点项目（ZD2020156）

Abstract: 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.

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