• DocumentCode
    2477303
  • Title

    Status Recognition for Electrical Parameters of ESPCP Based on Biomimetic Pattern Recognition

  • Author

    Shi Hai-tao ; Yu Yun-hua ; Kong Qian-qian

  • Author_Institution
    Coll. of Inf. & Control Eng., China Univ. of Pet. (East China), Dongying, China
  • fYear
    2010
  • fDate
    22-23 May 2010
  • Firstpage
    1
  • Lastpage
    3
  • Abstract
    Various fault types and difficult diagnosis restricted the improvement of economic benefit and system efficiency of electrical submersible progressing cavity pump (ESPCP) production system. A novel method for status recognition of electrical parameters in fault diagnosis of ESPCP based on biomimetic pattern recognition (BPR) is presented. Application results show the proposed BPR classifier produces significant accuracy for classification of ESPCP electrical parameters. Compared with the results based on support vector machine (SVM), the proposed method is more efficiency.
  • Keywords
    biomimetics; fault diagnosis; pattern recognition; pumps; ESPCP electrical parameters; biomimetic pattern recognition; electrical submersible progressing cavity pump production system; fault diagnosis; support vector machine; Biomimetics; Business process re-engineering; Electrostatic precipitators; Frequency; Neurons; Pattern recognition; Petroleum; Shape; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-5872-1
  • Electronic_ISBN
    978-1-4244-5874-5
  • Type

    conf

  • DOI
    10.1109/IWISA.2010.5473236
  • Filename
    5473236