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EEMD和分形组合技术对ECS涡轮轴承故障特征提取的研究

李晨晨 韩清鹏 李天成 朱瑞

李晨晨, 韩清鹏, 李天成, 朱瑞. EEMD和分形组合技术对ECS涡轮轴承故障特征提取的研究[J]. 机械科学与技术, 2019, 38(1): 37-43. doi: 10.13433/j.cnki.1003-8728.20180108
引用本文: 李晨晨, 韩清鹏, 李天成, 朱瑞. EEMD和分形组合技术对ECS涡轮轴承故障特征提取的研究[J]. 机械科学与技术, 2019, 38(1): 37-43. doi: 10.13433/j.cnki.1003-8728.20180108
Li Chenchen, Han Qingpeng, Li Tiancheng, Zhu Rui. Study on Fault Feature Extraction of ECS Turbine Bearing by Combination of EEMD and Correlation Dimension[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(1): 37-43. doi: 10.13433/j.cnki.1003-8728.20180108
Citation: Li Chenchen, Han Qingpeng, Li Tiancheng, Zhu Rui. Study on Fault Feature Extraction of ECS Turbine Bearing by Combination of EEMD and Correlation Dimension[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(1): 37-43. doi: 10.13433/j.cnki.1003-8728.20180108

EEMD和分形组合技术对ECS涡轮轴承故障特征提取的研究

doi: 10.13433/j.cnki.1003-8728.20180108
基金项目: 

国家自然科学基金项目 11502140

详细信息
    作者简介:

    李晨晨(1992-), 硕士研究生, 研究方向为机械故障诊断, 振动信号分析, liwind@126.com

    通讯作者:

    韩清鹏, 副教授, 硕士生导师, han1011@163.com

  • 中图分类号: TN199.72

Study on Fault Feature Extraction of ECS Turbine Bearing by Combination of EEMD and Correlation Dimension

  • 摘要: 针对飞机环控涡轮轴承运行时的非线性动力学特性,为了更加准确地分析轴承的故障,从振动信号分析的角度,提出基于EEMD和分形维数相结合的轴承状态特征量提取方法。先对轴承正常、内圈故障、外圈故障和保持架故障等不同运行状态下的振动信号进行EEMD分解,滤除噪声信号,提高信噪比,以减小背景噪声对分形的不利影响。然后对去噪信号再进行相空间重构,计算其关联维数并进行对比分析。实验结果表明:关联维数作为非线性几何不变量可以作为环控涡轮轴承运行状态的特征量;该方法能够准确有效地识别轴承的运行状态。
  • 图  1  仿真信号

    图  2  EMD分解结果

    图  3  EEMD分解结果

    图  4  实验平台转子系统

    图  5  内圈和外圈故障示意图

    图  6  第1组不同工况下振动信号时频图

    图  7  第1组正常信号EEMD分解

    图  8  不同嵌入维数m时的关联积分曲线

    图  9  4种工况振动信号关联积分曲线图

    图  10  4种工况的分形积分图

    图  11  4种工况8组信号的关联维数

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出版历程
  • 收稿日期:  2018-01-07
  • 刊出日期:  2019-01-05

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