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ITD-多尺度熵和ELM的风电轴承健康状态识别

张朝林 范玉刚 冯早

张朝林, 范玉刚, 冯早. ITD-多尺度熵和ELM的风电轴承健康状态识别[J]. 机械科学与技术, 2018, 37(11): 1731-1736. doi: 10.13433/j.cnki.1003-8728.20180121
引用本文: 张朝林, 范玉刚, 冯早. ITD-多尺度熵和ELM的风电轴承健康状态识别[J]. 机械科学与技术, 2018, 37(11): 1731-1736. doi: 10.13433/j.cnki.1003-8728.20180121
Zhang Zhaolin, Fan Yugang, Feng Zao. Health Status Recognition of Wind Turbine Bearings based on ITD-MSE and ELM[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(11): 1731-1736. doi: 10.13433/j.cnki.1003-8728.20180121
Citation: Zhang Zhaolin, Fan Yugang, Feng Zao. Health Status Recognition of Wind Turbine Bearings based on ITD-MSE and ELM[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(11): 1731-1736. doi: 10.13433/j.cnki.1003-8728.20180121

ITD-多尺度熵和ELM的风电轴承健康状态识别

doi: 10.13433/j.cnki.1003-8728.20180121
基金项目: 

国家自然科学基金项目(61741310)资助

详细信息
    作者简介:

    张朝林(1991-),硕士研究生,研究方向为故障诊断,zzldpn@vip.qq.com

    通讯作者:

    范玉刚,副教授,硕士生导师,ygfan@qq.com

Health Status Recognition of Wind Turbine Bearings based on ITD-MSE and ELM

  • 摘要: 对风力发电机机组的运行状况进行实时监测,并识别其健康状态,是保证机组正常运行的关键,为此提出一种固有时间尺度分解(Intrinsic time-scale decomposition,ITD)-多尺度熵(Multiscale entropy,MSE)的振动信号分析方法,对振动信号进行预处理,提取重构信号时域特征,并结合极限学习机(Extreme learning machine,ELM)对风电轴承健康状态进行识别。首先采用ITD方法对风电轴承的振动信号进行分解,得到一系列固有旋转分量,并计算其多尺度熵值,以多尺度熵值大小为依据,选取固有旋转分量并进行信号重构。计算重构信号的均方根值、峭度值、峰值因子与峰峰值,并将其作为特征指标值,建立ELM识别模型,识别风电轴承的健康状态。风电轴承试验结果表明,本文模型可以准确识别风电轴承健康状态。
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出版历程
  • 收稿日期:  2017-11-28
  • 刊出日期:  2018-11-05

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