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HDLMD及JRD在滚动轴承性能评估中的应用

罗亭 王晓东 杨创艳 李卓睿

罗亭, 王晓东, 杨创艳, 李卓睿. HDLMD及JRD在滚动轴承性能评估中的应用[J]. 机械科学与技术, 2021, 40(7): 1000-1008. doi: 10.13433/j.cnki.1003-8728.20200166
引用本文: 罗亭, 王晓东, 杨创艳, 李卓睿. HDLMD及JRD在滚动轴承性能评估中的应用[J]. 机械科学与技术, 2021, 40(7): 1000-1008. doi: 10.13433/j.cnki.1003-8728.20200166
LUO Ting, WANG Xiaodong, YANG Chuangyan, LI Zhuorui. Application of HDLMD and JRD in Performance Evaluation of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(7): 1000-1008. doi: 10.13433/j.cnki.1003-8728.20200166
Citation: LUO Ting, WANG Xiaodong, YANG Chuangyan, LI Zhuorui. Application of HDLMD and JRD in Performance Evaluation of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(7): 1000-1008. doi: 10.13433/j.cnki.1003-8728.20200166

HDLMD及JRD在滚动轴承性能评估中的应用

doi: 10.13433/j.cnki.1003-8728.20200166
基金项目: 

国家自然科学基金项目 51765022

国家自然科学基金项目 61663017

详细信息
    作者简介:

    罗亭(1995-), 硕士研究生, 研究方向为机械故障诊断与性能退化评估, 1172826742@qq.com

    通讯作者:

    王晓东, 教授, 博士生导师, wangxd9621@sina.com

  • 中图分类号: TN911.7;TH165.3

Application of HDLMD and JRD in Performance Evaluation of Rolling Bearing

  • 摘要: 针对微分局部均值分解(Differential local mean decomposition, DLMD)方法中微分次数计算缺乏理论指导以及传统性能退化指标无法准确表示滚动轴承在全寿命阶段上当前状态的问题, 提出了一种基于HDLMD(Hilbert-differential local mean decomposition, HDLMD)和JRD(Jensen-Renyi divergence)的滚动轴承性能评估方法。该方法首先对原始振动信号进行HDLMD分解, 提取乘积函数(Product function, PF)矩阵; 然后, 基于拉普拉斯分值(Laplacian score, LS)选择包含最多故障信息的PF分量; 再计算筛选之后的有效PF分量的概率分布, 得到有效PF分量的Renyi熵值; 最后, 计算正常信号与不同故障程度信号之间的JRD距离, 并判断滚动轴承的退化状态。通过凯西斯储大学(Case western reserve university, CWRU)滚动轴承实验数据和NASA(National aeronautics and space administration)全寿命周期数据实验表明, 本文所提方法可以准确、有效地评估轴承性能的退化状态。
  • 图  1  HDLMD分解算法流程图

    图  2  仿真信号的时域波形图

    图  3  仿真信号的分解结果图

    图  4  仿真信号微分Hilbert谱图

    图  5  仿真信号LMD和HDLMD分解分量的功率谱图

    图  6  性能退化评估方法流程图

    图  7  美国CWRU轴承数据中心实验台

    图  8  内圈故障信号时域波形

    图  9  内圈故障信号HDLMD分解结果

    图  10  HDLMD分解的Renyi熵值

    图  11  HDLMD分解JRD距离

    图  12  LMD分解的Renyi熵值

    图  13  LMD分解的JRD距离

    图  14  轴承全寿命周期数据实验平台

    图  15  全寿命周期数据波形图

    图  16  全寿命周期数据Renyi熵图

    图  17  HDLMD分解JRD距离图

    图  18  LMD分解JRD距离图

    表  1  各PF分量的拉普拉斯分值LS

    PF1 PF2 PF3 PF4 PF5 PF6 PF7
    0.767 9 0.816 4 0.643 6 0.977 7 1.988 3 4.829 5 6.575 9
    下载: 导出CSV

    表  2  HDLMD和LMD分解的JRD方差对比

    方法 方差
    HDLMD分解JRD 5.390 7×10-4
    LMD分解JRD 9.355 1×10-4
    下载: 导出CSV
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  • 收稿日期:  2019-01-15
  • 刊出日期:  2021-07-01

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