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ELMD和MCKD在滚动轴承早期故障诊断中的应用

王朝阁 庞震 任学平 孙百祎 王建国

王朝阁, 庞震, 任学平, 孙百祎, 王建国. ELMD和MCKD在滚动轴承早期故障诊断中的应用[J]. 机械科学与技术, 2017, 36(11): 1764-1770. doi: 10.13433/j.cnki.1003-8728.2017.1121
引用本文: 王朝阁, 庞震, 任学平, 孙百祎, 王建国. ELMD和MCKD在滚动轴承早期故障诊断中的应用[J]. 机械科学与技术, 2017, 36(11): 1764-1770. doi: 10.13433/j.cnki.1003-8728.2017.1121
Wang Chaoge, Pang Zhen, Ren Xueping, Sun Baiyi, Wang Jianguo. Early Fault Diagnosis of Roller Bearing based on Ensemble Local Mean Decomposition and Maximum Correlated Kurtosis Deconvolution[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(11): 1764-1770. doi: 10.13433/j.cnki.1003-8728.2017.1121
Citation: Wang Chaoge, Pang Zhen, Ren Xueping, Sun Baiyi, Wang Jianguo. Early Fault Diagnosis of Roller Bearing based on Ensemble Local Mean Decomposition and Maximum Correlated Kurtosis Deconvolution[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(11): 1764-1770. doi: 10.13433/j.cnki.1003-8728.2017.1121

ELMD和MCKD在滚动轴承早期故障诊断中的应用

doi: 10.13433/j.cnki.1003-8728.2017.1121
基金项目: 

国家自然科学基金项目(21366017)、内蒙古高等学校科学研究项目(NYZY16154)及内蒙古科技大学创新基金项目(2015QDL11)资助

详细信息
    作者简介:

    王朝阁(1992-),硕士研究生,研究方向为机械设备故障诊断及状态检测,wangchaoge1992@163.com

    通讯作者:

    任学平(联系人),教授,博士,rxp@imust.cn

Early Fault Diagnosis of Roller Bearing based on Ensemble Local Mean Decomposition and Maximum Correlated Kurtosis Deconvolution

  • 摘要: 针对滚动轴承早期故障特征信号微弱且受环境噪声影响严重,故障特征信息难以识别的问题,提出了基于总体局部均值分解(Ensemble local mean decomposition,ELMD)和最大相关峭度反褶积(Maximum correlated kurtosis deconvolution,MCKD)的早期故障诊断方法。该方法首先运用ELMD对采集到的振动信号进行分解,得到有限个乘积函数(Product function,PF),由于噪声的干扰,从PF分量的频谱中很难对故障做出正确的判断。然后对包含故障特征的PF分量进行最大相关峭度反褶积处理以消除噪声影响,凸现故障特征信息。最后对降噪信号进行Hilbert包络谱分析,即可从中准确地识别出轴承的故障特征频率。通过轴承故障模拟实验和工程应用实例验证了该方法的有效性与优越性。
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出版历程
  • 收稿日期:  2016-06-10
  • 刊出日期:  2017-11-05

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