• DocumentCode
    3778020
  • Title

    Machine fault diagnosis using industrial wireless sensor networks and support vector machine

  • Author

    Bai Jie;Liqun Hou; Ma Yongguang

  • Author_Institution
    Department of Automation, North China Electric Power University, Baoding 071003, China
  • Volume
    1
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    153
  • Lastpage
    158
  • Abstract
    A machine fault diagnosis method using industrial wireless sensor networks (IWSNs) and support vector machine (SVM) is presented in this paper as a potential low-cost and effective solution for device condition monitoring and fault diagnosis. On sensor node SVM is proposed and researched to reduce the data transmission between sensor nodes, decrease node energy consumption and increase the fault diagnosis accuracy. Simulation experiments are carried out to verify the proposed method. The simulation results show SVM has strong ability to learn and recognize the machine fault pattern.
  • Keywords
    "Support vector machines","Fault diagnosis","Induction motors","Feature extraction","Monitoring","Training","Wireless sensor networks"
  • Publisher
    ieee
  • Conference_Titel
    Electronic Measurement & Instruments (ICEMI), 2015 12th IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/ICEMI.2015.7494241
  • Filename
    7494241