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卷积神经网络和峭度在轴承故障诊断中的应用

李俊 刘永葆 余又红

李俊, 刘永葆, 余又红. 卷积神经网络和峭度在轴承故障诊断中的应用[J]. 航空动力学报, 2019, 34(11): 2423-2431. doi: 10.13224/j.cnki.jasp.2019.11.014
引用本文: 李俊, 刘永葆, 余又红. 卷积神经网络和峭度在轴承故障诊断中的应用[J]. 航空动力学报, 2019, 34(11): 2423-2431. doi: 10.13224/j.cnki.jasp.2019.11.014
LI Jun, LIU Yongbao, YU Youhong. Application of convolutional neural network and kurtosis in fault diagnosis of rolling bearing[J]. Journal of Aerospace Power, 2019, 34(11): 2423-2431. doi: 10.13224/j.cnki.jasp.2019.11.014
Citation: LI Jun, LIU Yongbao, YU Youhong. Application of convolutional neural network and kurtosis in fault diagnosis of rolling bearing[J]. Journal of Aerospace Power, 2019, 34(11): 2423-2431. doi: 10.13224/j.cnki.jasp.2019.11.014

卷积神经网络和峭度在轴承故障诊断中的应用

doi: 10.13224/j.cnki.jasp.2019.11.014
基金项目: 海军工程大学自然科学自主立项项目(425317K004,425317K137)

Application of convolutional neural network and kurtosis in fault diagnosis of rolling bearing

  • 摘要: 针对传统智能诊断方法依靠专家知识和人工提取数据特征工作量大的问题,结合深度学习方法在特征提取和处理大数据方面的优势,研究了一种基于卷积神经网络和振动信号峭度指标的滚动轴承故障诊断方法。该方法将深度学习应用于轴承故障诊断,提取滚动轴承正常状态、内圈故障、外圈故障和滚动体故障4种状态的振动信号,将振动信号分段处理得到峭度指标,使用数据到图像的转换方法将峭度指标转换为灰度图,送入卷积神经网络模型完成故障分类。在进行滚动轴承故障诊断的实验时,所提的模型诊断准确率达到99.5%,高于传统支持向量机(SVM)算法的95.8%。

     

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出版历程
  • 收稿日期:  2019-05-24
  • 刊出日期:  2019-11-28

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