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改进近似函数的重构算法在滚动轴承故障信号中的应用研究

钱秋亮 董宝伟 邵馨叶 邵建龙 朱荣

钱秋亮,董宝伟,邵馨叶, 等. 改进近似函数的重构算法在滚动轴承故障信号中的应用研究[J]. 机械科学与技术,2021,40(11):1747-1753 doi: 10.13433/j.cnki.1003-8728.20200280
引用本文: 钱秋亮,董宝伟,邵馨叶, 等. 改进近似函数的重构算法在滚动轴承故障信号中的应用研究[J]. 机械科学与技术,2021,40(11):1747-1753 doi: 10.13433/j.cnki.1003-8728.20200280
QIAN Qiuliang, DONG Baowei, SHAO Xinye, SHAO Jianlong, ZHU Rong. Application of Reconstruction Algorithm of Improved Approximate Function in Fault Signal of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(11): 1747-1753. doi: 10.13433/j.cnki.1003-8728.20200280
Citation: QIAN Qiuliang, DONG Baowei, SHAO Xinye, SHAO Jianlong, ZHU Rong. Application of Reconstruction Algorithm of Improved Approximate Function in Fault Signal of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(11): 1747-1753. doi: 10.13433/j.cnki.1003-8728.20200280

改进近似函数的重构算法在滚动轴承故障信号中的应用研究

doi: 10.13433/j.cnki.1003-8728.20200280
基金项目: 国家自然科学基金项目(61971208)、昆明理工大学慕课及金课建设项目(2019080211,20171113)及昆明理工大学信息工程与自动化学院教育教学改革建设项目(20191001,20180507)
详细信息
    作者简介:

    钱秋亮(1994−),硕士研究生,研究方向为智能信息处理。250922566@qq.com

    通讯作者:

    邵建龙,教授,硕士生导师,sj-long@163.com

  • 中图分类号: TH133.3

Application of Reconstruction Algorithm of Improved Approximate Function in Fault Signal of Rolling Bearing

  • 摘要: 为改进优化压缩感知理论中以凸优化方式的重构算法在滚动轴承故障信号的应用中存在重构误差较大、重构迭代次数多及重构误差信噪比低等问题。本文提出采用反正弦函数取代双曲函数近似逼近l0范数,使得函数曲线与l0范数的逼近程度更高且更为光滑,同时加入衰减因子,加快迭代速度。实验结果表明该算法加入衰减因子后在一定程度上减少了迭代次数,却损失了部分重构精度,但整体重构效果相对已有算法具有重构精度高、迭代次数少及重构信噪比高的优势。
  • 图  1  $\sigma = 0.1$时4种函数对比

    图  2  SL0算法的信号重构

    图  3  NSL0算法的信号重构

    图  4  ONSL0算法的信号重构

    图  5  15 dB噪声下的SL0算法的信号重构

    图  6  15 dB噪声下的NSL0算法的信号重构

    图  7  15 dB噪声下的ONSL0算法的信号重构

    图  8  不同压缩比的SRNR对比

    图  9  各算法重构稀疏系数对比

    图  10  各算法重构用时及误差对比

    表  1  无噪声信号重构误差

    SL0NSL0ONSL0
    0.00701.083 5×10−51.171 0×10−13
    下载: 导出CSV

    表  2  加入15 dB噪声信号重构误差

    SL0NSL0ONSL0
    3.08945.25792.3134
    下载: 导出CSV

    表  3  各算法重构用时及误差对比

    算法SL0NSL0ONSL0
    迭代次数 30 40 13
    相对误差0.0017450.0004930.000227
    下载: 导出CSV

    表  4  各算法重构用时及误差对比

    算法SNR时间/s误差/(m·s−2)
    SL0 62.9446 0.1708 0.0159
    NSL0 31.1427 0.1565 0.0510
    ONSL0(含衰减) 64.5242 0.1738 0.0125
    ONSL0(无衰减) 261.1287 0.3681 1.854 5×10−12
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-16
  • 刊出日期:  2021-11-05

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