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Degradation trend estimation and prognosis of large low speed slewing bearing lifetime

Journal Article


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Abstract


  • In many applications, degradation of bearing conditions is usually monitored by changes

    in time-domain features. However, in low speed (< 10 rpm) slewing bearing, these changes are not

    easily detected because of the low energy and low frequency of the vibration. To overcome this

    problem, a combined low pass filter (LPF) and adaptive line enhancer (ALE) signal preconditioning

    method is used. Time-domain features such as root mean square (RMS), skewness and

    kurtosis are extracted from the output signal of the combined LPF and ALE method. The extracted

    features show accurate information about the incipient of fault as compared to extracted features

    from the original vibration signal. This information then triggers the prognostic algorithm to predict

    the remaining lifetime of the bearing. The algorithm used to determine the trend of the nonstationary

    data is auto-regressive integrated moving average (ARIMA).

Authors


  •   Kosasih, Buyung B.
  •   Caesarendra, Wahyu (external author)
  •   Tieu, A Kiet.
  •   Widodo, Achmad (external author)
  •   Moodie, Craig A. S. (external author)

Publication Date


  • 2014

Citation


  • Kosasih, P., Caesarendra, W., Tieu, A. K., Widodo, A. & Moodie, C. A. S. (2014). Degradation trend estimation and prognosis of large low speed slewing bearing lifetime. Applied Mechanics and Materials, 493 343-348.

Scopus Eid


  • 2-s2.0-84892881652

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2854&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1845

Number Of Pages


  • 5

Start Page


  • 343

End Page


  • 348

Volume


  • 493

Abstract


  • In many applications, degradation of bearing conditions is usually monitored by changes

    in time-domain features. However, in low speed (< 10 rpm) slewing bearing, these changes are not

    easily detected because of the low energy and low frequency of the vibration. To overcome this

    problem, a combined low pass filter (LPF) and adaptive line enhancer (ALE) signal preconditioning

    method is used. Time-domain features such as root mean square (RMS), skewness and

    kurtosis are extracted from the output signal of the combined LPF and ALE method. The extracted

    features show accurate information about the incipient of fault as compared to extracted features

    from the original vibration signal. This information then triggers the prognostic algorithm to predict

    the remaining lifetime of the bearing. The algorithm used to determine the trend of the nonstationary

    data is auto-regressive integrated moving average (ARIMA).

Authors


  •   Kosasih, Buyung B.
  •   Caesarendra, Wahyu (external author)
  •   Tieu, A Kiet.
  •   Widodo, Achmad (external author)
  •   Moodie, Craig A. S. (external author)

Publication Date


  • 2014

Citation


  • Kosasih, P., Caesarendra, W., Tieu, A. K., Widodo, A. & Moodie, C. A. S. (2014). Degradation trend estimation and prognosis of large low speed slewing bearing lifetime. Applied Mechanics and Materials, 493 343-348.

Scopus Eid


  • 2-s2.0-84892881652

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2854&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1845

Number Of Pages


  • 5

Start Page


  • 343

End Page


  • 348

Volume


  • 493