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基于多尺度熵的滚动轴承Elman神经网络故障诊断方法

张龙 张磊 熊国良 周继惠 王宁 王明翔

张龙, 张磊, 熊国良, 周继惠, 王宁, 王明翔. 基于多尺度熵的滚动轴承Elman神经网络故障诊断方法[J]. 机械科学与技术, 2014, 33(12): 1854-1858. doi: 10.13433/j.cnki.1003-8728.2014.1219
引用本文: 张龙, 张磊, 熊国良, 周继惠, 王宁, 王明翔. 基于多尺度熵的滚动轴承Elman神经网络故障诊断方法[J]. 机械科学与技术, 2014, 33(12): 1854-1858. doi: 10.13433/j.cnki.1003-8728.2014.1219
Zhang Long, Zhang Lei, Xiong Guoliang, Zhou Jihui, Wang Ning, Wang Mingxiang. Rolling Bearing Fault Diagnosis Based on Multiscale Entropy and Elman Neural Network[J]. Mechanical Science and Technology for Aerospace Engineering, 2014, 33(12): 1854-1858. doi: 10.13433/j.cnki.1003-8728.2014.1219
Citation: Zhang Long, Zhang Lei, Xiong Guoliang, Zhou Jihui, Wang Ning, Wang Mingxiang. Rolling Bearing Fault Diagnosis Based on Multiscale Entropy and Elman Neural Network[J]. Mechanical Science and Technology for Aerospace Engineering, 2014, 33(12): 1854-1858. doi: 10.13433/j.cnki.1003-8728.2014.1219

基于多尺度熵的滚动轴承Elman神经网络故障诊断方法

doi: 10.13433/j.cnki.1003-8728.2014.1219
基金项目: 

国家自然科学基金项目(51205130,51265010)

江西省教育厅科技项目(GJJ12318)

华东交通大学校立科研基金项目(13JD07)

江西省自然科学基金项目(20132BAB216029)

江西省研究生创新专项基金项目(YC2014-S244,YC2014-S265)资助

详细信息
    作者简介:

    张龙(1980- ),讲师,博士,研究方向为机械故障诊断,longzh@126.com。

Rolling Bearing Fault Diagnosis Based on Multiscale Entropy and Elman Neural Network

  • 摘要: 针对滚动轴承故障振动信号具有跨尺度复杂性的特点,提出了一种新的基于多尺度熵(multiscale entropy,MSE)和反馈式Elman神经网络的滚动轴承故障诊断方法.该方法利用MSE对滚动轴承不同健康状态下的振动信号进行故障特征提取,并将其作为Elman神经网络的输入,利用Elman神经网络自动识别轴承所属的故障类型及故障程度.实验数据包括不同故障类型和不同故障程度样本,结果表明提出的方法能有效地实现滚动轴承故障类型以及程度的智能诊断,效果优于前馈式概率神经网络(Probabilistic neural network,PNN),并具有较低的虚警率和漏警率.
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出版历程
  • 收稿日期:  2013-12-02
  • 刊出日期:  2014-12-05

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