Bearing Fault Detection within Wind Turbine Gearbox

S. Shanbr, F. Elasha, Mohamed Elforjani, J.A. Teixeira

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    5 Citations (Scopus)


    One of the most challenging scenarios in condition monitoring is the bearing fault detection within strongly masked signal, where the vibration or acoustic signal is dominated by other component such as gears and shafts, a good example for this scenario is the wind turbine gearbox which presents one of the difficult bearing detection tasks. Nonstationary signal analysis is considered one of the main topics in field of machinery fault diagnosis. A set of signal processing techniques has been studied to investigate their feasibility for bearing fault detection in wind turbine gearbox. These techniques include statistical condition indicators, Spectral kurtosis, and envelope analysis. The result of vibration analysis showed the possibility of bearing detection in wind turbine high speed shaft using multiple signal processing techniques. However, among all these signal processing techniques, Spectral Kurtosis followed by envelope analysis provides early fault detection compared to the other techniques employed.
    Original languageEnglish
    Title of host publication2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
    Subtitle of host publicationIEEE
    Number of pages6
    ISBN (Electronic)978-1-5090-4020-9
    ISBN (Print)978-1-5090-4021-6
    Publication statusPublished - 14 Dec 2017
    EventInternational Conference on Sensing, Diagnostics, Prognostics, and Control - Shanghai, China
    Duration: 16 Aug 201718 Aug 2017


    ConferenceInternational Conference on Sensing, Diagnostics, Prognostics, and Control
    Abbreviated titleSDPC 2017


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