1044 / 2019-05-19 10:55:26
Fault Identification of Hydroelectric Sets Based on Time-frequency Diagram and Convolutional Neural Network
hydroelectric sets , fault diagnosis , time-frequency transform , time-frequency diagram , convolutional neural network
全文录用
Hui Li / Xi’an University of Technology
Qiangbin Meng / Xi’an University of Technology
Xintong Li / Shaanxi Gas Group
Rong Jia / Xi'an University of Technology
Jian Dang / Xi'an University of Technology
Aiming at the poor generalization ability of traditional hydropower unit fault diagnosis methods, a fault diagnosis method for hydroelectric sets based on time-frequency diagram and convolutional neural network(CNN) is proposed. First, the hydroelectric sets vibration signal is time-frequency transformed to construct a time-frequency diagram. Then, combined with the convolutional neural network, the fault state identification of the hydropower unit is realized. The method realizes the automatic extraction of the texture features of the time-frequency diagram, avoids manual identification, and can quickly and accurately identify the state of the hydropower unit. The results show that the method can effectively identify the type of fault.
重要日期
  • 会议日期

    10月21日

    2019

    10月24日

    2019

  • 10月13日 2019

    摘要录用通知日期

  • 10月13日 2019

    初稿截稿日期

  • 10月14日 2019

    初稿录用通知日期

  • 10月24日 2019

    注册截止日期

  • 10月29日 2019

    终稿截稿日期

承办单位
Xi'an Jiaotong University
联系方式
移动端
在手机上打开
小程序
打开微信小程序
客服
扫码或点此咨询