• DocumentCode
    130896
  • Title

    Signal extraction and fault identification of steam turbine vibration

  • Author

    Junru Gao ; Xin Meng ; Yajun Sun

  • Author_Institution
    Hebei Univ. of Eng. Handan, Handan, China
  • fYear
    2014
  • fDate
    27-29 June 2014
  • Firstpage
    481
  • Lastpage
    483
  • Abstract
    This paper, the vibration signal of steam turbine which are detected by fault diagnosis are influenced by environmental noise and detecting instrument itself, leading to vibration signal waveform distortion which contains a large number of non-stationary composition, and cannot effectively react turbine fault characteristics, and the coupling among different fault characteristics of unilateral fault features make it difficult to identify fault accurately. Aiming at solving this problem, this paper combine the axis of spectrum analysis with path analysis of vibration signal processing and recognition method, two kinds of detection method in the fault diagnosis process validation to ensure the accuracy of test results.
  • Keywords
    distortion; fault diagnosis; mechanical engineering computing; signal detection; spectral analysis; steam turbines; vibrations; environmental noise; fault diagnosis; fault identification; nonstationary composition; path analysis; signal detection method; signal extraction; spectrum analysis; steam turbine vibration signal; turbine fault characteristics; unilateral fault features; vibration signal processing; vibration signal recognition; vibration signal waveform distortion; Fault diagnosis; Feature extraction; Rotors; Turbines; Vibrations; Wavelet analysis; Wavelet packets; Axis trajectory; Fault diagnosis; Spectrum; Steam turbine unit; vibration signal;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on
  • Conference_Location
    Beijing
  • ISSN
    2327-0586
  • Print_ISBN
    978-1-4799-3278-8
  • Type

    conf

  • DOI
    10.1109/ICSESS.2014.6933610
  • Filename
    6933610