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自适应随机共振和DEMD的单向阀早期故障诊断

牟竹青 冯早 黄国勇 范玉刚

牟竹青, 冯早, 黄国勇, 范玉刚. 自适应随机共振和DEMD的单向阀早期故障诊断[J]. 机械科学与技术, 2018, 37(4): 537-544. doi: 10.13433/j.cnki.1003-8728.2018.0408
引用本文: 牟竹青, 冯早, 黄国勇, 范玉刚. 自适应随机共振和DEMD的单向阀早期故障诊断[J]. 机械科学与技术, 2018, 37(4): 537-544. doi: 10.13433/j.cnki.1003-8728.2018.0408
Mu Zhuqing, Feng Zao, Huang Guoyong, Fan Yugang. Early Fault Diagnosis of Check Valve with Adaptive Stochastic Resonance and DEMD[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(4): 537-544. doi: 10.13433/j.cnki.1003-8728.2018.0408
Citation: Mu Zhuqing, Feng Zao, Huang Guoyong, Fan Yugang. Early Fault Diagnosis of Check Valve with Adaptive Stochastic Resonance and DEMD[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(4): 537-544. doi: 10.13433/j.cnki.1003-8728.2018.0408

自适应随机共振和DEMD的单向阀早期故障诊断

doi: 10.13433/j.cnki.1003-8728.2018.0408
基金项目: 

国家自然科学基金项目(61663017)与云南省科技计划项目(2015ZC005)资助

详细信息
    作者简介:

    牟竹青(1991-),硕士研究生,研究方向为信号处理、故障诊断,1452304445@qq.com

    通讯作者:

    黄国勇,副教授,博士,42427566@qq.com

Early Fault Diagnosis of Check Valve with Adaptive Stochastic Resonance and DEMD

  • 摘要: 针对高压隔膜泵单向阀的早期故障振动信号信噪比(SNR)低,故障特征提取困难的问题,本文提出一种自适应随机共振和微分经验模态分解(DEMD)的早期故障诊断方法。首先对原信号进行预处理,设置压缩比进行变尺度处理;然后将SNR作为自适应度函数,利用粒子群(PSO)算法优化随机共振(SR)系统参数,将优化后参数及处理后的信号输入SR系统中;最后对系统输出的信号进行DEMD算法分解,对各分量进行频谱分析,选取含特征频率的分量合成进行包络分析,以提取故障特征信息。经仿真分析与工程实验表明,该方法能够较好地提取出单向阀的早期故障特征信息。
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出版历程
  • 收稿日期:  2017-05-03
  • 刊出日期:  2018-04-05

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