基于改进高斯随机测量矩阵的切削力信号压缩感知方法

doi:10.3969/j.issn.1004-132X.2021.18.012

中国机械工程 ›› 2021, Vol. 32 ›› Issue (18): 2231-2238.DOI: 10.3969/j.issn.1004-132X.2021.18.012

基于改进高斯随机测量矩阵的切削力信号压缩感知方法

吴凤和1,2;张宁1;李元祥1;张会龙1;郭保苏1,2

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

出版日期:2021-09-25 发布日期:2021-10-14
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）

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.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

摘要/Abstract

摘要： 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，再借助高效的压缩采样匹配追踪算法从测量值中恢复得到原始切削力信号。实验结果表明，改进的高斯随机测量矩阵具有更高的重构精度和稳定性，所提出的压缩感知方法在保证切削力数据重构效率和精度的同时，显著减少了数据量。

关键词: 切削力信号, 压缩感知, 测量矩阵, 压缩采样匹配追踪

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.

Key words: cutting force signal, compressed sensing, measurement matrix, compressive sampling matching pursuit

中图分类号:

TB931

吴凤和, 张宁, 李元祥, 张会龙, 郭保苏, . 基于改进高斯随机测量矩阵的切削力信号压缩感知方法[J]. 中国机械工程, 2021, 32(18): 2231-2238.

WU Fenghe, ZHANG Ning, LI Yuanxiang, ZHANG Huilong, GUO Baosu, . Compressed Sensing Method for Cutting Force Signals Based on Improved Gauss Random Measurement Matrix[J]. China Mechanical Engineering, 2021, 32(18): 2231-2238.

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链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2021.18.012

http://www.cmemo.org.cn/CN/Y2021/V32/I18/2231

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[1]	王珂1，2;吕勇1，2;易灿灿1，2. 压缩感知框架下的共振解调故障诊断方法[J]. 中国机械工程, 2018, 29(16): 1907-1911.
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基于改进高斯随机测量矩阵的切削力信号压缩感知方法

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

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