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自适应随机共振与ELMD在轴承故障诊断中的应用

何园园 张超 陈帅

何园园, 张超, 陈帅. 自适应随机共振与ELMD在轴承故障诊断中的应用[J]. 机械科学与技术, 2018, 37(4): 607-613. doi: 10.13433/j.cnki.1003-8728.2018.0418
引用本文: 何园园, 张超, 陈帅. 自适应随机共振与ELMD在轴承故障诊断中的应用[J]. 机械科学与技术, 2018, 37(4): 607-613. doi: 10.13433/j.cnki.1003-8728.2018.0418
He Yuanyuan, Zhang Chao, Chen Shuai. The Application of Self-adaptive Stochastic Resonance and ELMD in Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(4): 607-613. doi: 10.13433/j.cnki.1003-8728.2018.0418
Citation: He Yuanyuan, Zhang Chao, Chen Shuai. The Application of Self-adaptive Stochastic Resonance and ELMD in Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(4): 607-613. doi: 10.13433/j.cnki.1003-8728.2018.0418

自适应随机共振与ELMD在轴承故障诊断中的应用

doi: 10.13433/j.cnki.1003-8728.2018.0418
基金项目: 

国家自然科学基金项目(51565046)、内蒙古自然科学基金项目(2015MS0512)及内蒙古高等学校科学研究项目(NJZY146)资助

详细信息
    作者简介:

    何园园(1991-),硕士研究生,研究方向为机电系统智能诊断,1042346395@qq.com

    通讯作者:

    张超,副教授,博士,硕士生导师,zhanghero123@163.com

The Application of Self-adaptive Stochastic Resonance and ELMD in Bearing Fault Diagnosis

  • 摘要: 针对随机共振(Stochastic resonance,SR)在处理轴承故障信号时需要满足小参数(信号频率、幅值、噪声强度远小于1)这一条件以及轴承故障特征难以提取的问题,提出基于自适应变尺度随机共振与总体局部均值分解(Ensemble local mean decomposition,ELMD)的轴承故障诊断方法。首先,对实测的信号按照一定的频率进行压缩,使其满足随机共振小参数的要求,然后,通过遗传算法(Genetic algorithm,GA)对变尺度随机共振双稳系统中的结构参数a,b进行优化,最后将随机共振输出信号进行ELMD分解,通过各PF分量的频谱图寻找轴承故障特征频率。对实测轴承故障信号的实验分析,结果表明本文提出的方法可有效地应用于轴承的故障诊断中。
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出版历程
  • 收稿日期:  2017-05-18
  • 刊出日期:  2018-04-05

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