• DocumentCode
    2119771
  • Title

    Fault Diagnosis for Reciprocating Air Compressor Valve Using P-V Indicator Diagram and SVM

  • Author

    Wang, Fengtao ; Song, Lutao ; Zhang, Liang ; Li, Haifeng

  • Author_Institution
    Res. Inst. of Vibration, Dalian Univ. of Technol., Dalian, China
  • fYear
    2010
  • fDate
    24-26 Dec. 2010
  • Firstpage
    255
  • Lastpage
    258
  • Abstract
    This paper presents a method of fault diagnosis for the reciprocating air compressor valve based on the indicator diagram and the support vector machine (SVM). This paper strikes 7 invariant moments of the indicator diagram of reciprocating air compressor, using image processing methods, according to the same moment theory. Then the method can be used to extract effective features as feature vectors for training the support vector machine, and achieve fault diagnosis for reciprocating air compressor valve. Finally, the paper simulate 5 kinds of working conditions of valve to identify using the fault monitoring system of reciprocating compressor valve, in order to verify the feasibility and effectiveness of the method.
  • Keywords
    compressors; condition monitoring; fault diagnosis; feature extraction; image processing; indicators; learning (artificial intelligence); mechanical engineering computing; support vector machines; valves; P-V indicator diagram; SVM; fault diagnosis; fault monitoring system; feature extraction; feature vector; image processing method; moment theory; reciprocating air compressor valve; support vector machine; Fault diagnosis; Feature extraction; Image coding; Support vector machines; Training; Valves; Vibrations; SVM; indicator diagram; reciprocating air compressor; valve failure;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Engineering (ISISE), 2010 International Symposium on
  • Conference_Location
    Shanghai
  • ISSN
    2160-1283
  • Print_ISBN
    978-1-61284-428-2
  • Type

    conf

  • DOI
    10.1109/ISISE.2010.91
  • Filename
    5945097