• DocumentCode
    2941100
  • Title

    EEMD-1.5 Dimension Spectrum Applied to Locomotive Gear Fault Diagnosis

  • Author

    Chen, Lue ; Zi, Yanyang ; Cheng, Wei ; He, Zhengjia

  • Author_Institution
    State Key Lab. for Manuf. Syst., Xi´´an Jiaotong Univ., Xi´´an, China
  • Volume
    1
  • fYear
    2009
  • fDate
    11-12 April 2009
  • Firstpage
    622
  • Lastpage
    625
  • Abstract
    The criterion of adding white noise in Ensemble Empirical Mode Decomposition (EEMD) method is established. EEMD, used for avoiding mode mixing in signal decomposition, is combined with 1.5 dimension spectrum, which is the bispectrum diagonal slice to eliminate white noise and extract nonlinear coupling feature. A new method of EEMD-1.5 dimension spectrum for fault feature extraction is proposed. Firstly, vibration signal is adaptively anti alias decomposed by EEMD to get Intrinsic Mode Functions (IMFs). Then, 1.5 dimension spectrum is adopted to process IMFs which contain fault feature. Finally, EEMD-1.5 dimension spectrum is introduced into monitoring diagnosis of the gear box of the locomotive running gear, the results show that it successfully extracts an early gear crack fault feature.
  • Keywords
    fault diagnosis; feature extraction; gears; locomotives; time-frequency analysis; EEMD-1.5 dimension spectrum; Ensemble Empirical Mode Decomposition; fault feature extraction; intrinsic mode functions; locomotive gear fault diagnosis; nonlinear coupling feature; signal decomposition; Couplings; Data mining; Fault diagnosis; Feature extraction; Frequency; Gears; Noise measurement; Signal analysis; Signal processing; White noise; 1.5 dimension spectrum; EEMD; EMD; feature extraction; gear crack fault.;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
  • Conference_Location
    Zhangjiajie, Hunan
  • Print_ISBN
    978-0-7695-3583-8
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2009.53
  • Filename
    5203049