• DocumentCode
    572353
  • Title

    Intelligent Fault Diagnosis Method in Controlled Rectifier Based on Support Vector Machines

  • Author

    Lan Hai ; Liu Hong-Da ; Yue Wen-jie ; Shen Nai-jun

  • Author_Institution
    Coll. of Autom., Harbin Eng. Univ., Harbin, China
  • fYear
    2012
  • fDate
    27-29 March 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Rectifier units is widely used in electrochemical and the metallurgical industry,stability and reliability are required.This paper studied the fault classification and diagnosis method of the bridge 6-pulse waveform controlled rectifier circuit based on Support Vector Machines.All the faults in thyristors were analyzed, the fault classification method according to the rectifier aberrant voltage waveforms was put forward,fault voltage waveforms were summarized.By keeping the relations between faults and waveforms in a support vector machines model, the support vector machines model could be trained to detect faults and realize automation of fault diagnosis. Three-phase bridge rectifier circuit fault was presented as an example, coupled with the results of a certain power electronic circuit experiment, indicates that the method can accurately diagnose and locate fault for controlled rectifier circuits.
  • Keywords
    fault diagnosis; rectifiers; support vector machines; thyristor applications; bridge 6-pulse waveform controlled rectifier circuit; electrochemical industry; fault classification; fault detection; fault voltage waveforms; intelligent fault diagnosis method; metallurgical industry; power electronic circuit experiment; rectifier aberrant voltage waveforms; rectifier units; support vector machines; three-phase bridge rectifier circuit fault; thyristors; Circuit faults; Fault diagnosis; Integrated circuit modeling; Power electronics; Rectifiers; Support vector machines; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific
  • Conference_Location
    Shanghai
  • ISSN
    2157-4839
  • Print_ISBN
    978-1-4577-0545-8
  • Type

    conf

  • DOI
    10.1109/APPEEC.2012.6307674
  • Filename
    6307674