• vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1390/01/01
    • تاریخ انتشار در تی پی بین: 1390/01/01
    • تعداد بازدید: 522
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -

    time–frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. commonly-used signal analysis techniques, based on spectral approaches such as the fast fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. although the short-time fourier transform compensates well for the loss of time information incurred by the fast fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. to mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time fourier transforms in effectively analyzing non-stationary signals. these wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. in addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults.

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