Abstract
The rapid expansion of wind farms has accelerated research into improving the reliability of wind turbines to reduce operational and maintenance costs. A critical component in wind turbine drive-trains is the gearbox, which is prone to different types of failures due to long-term operation under tough environments, variable speeds and alternating loads. To detect gearbox fault early, a method is proposed for an effective fault diagnosis by using improved ensemble empirical mode decomposition (EEMD) and Hilbert square demodulation (HSD). The method was verified numerically by implementing the scheme on the vibration signals measured from bearing and gear test rigs. In the implementation process, the following steps were identified as being important: (1) in order to increase the accuracy of EEMD, a criterion of selecting the proper resampling frequency for raw vibration signals was developed; (2) to select the fault related intrinsic mode function (IMF) that had the biggest kurtosis index value, the resampled signal was decomposed into a series of IMFs; (3) the selected IMF was demodulated by means of HSD, and fault feature information could finally be obtained. The experimental results demonstrate the merit of the proposed method in gearbox fault diagnosis.
Highlights
In order to harvest wind energy more efficiently, wind turbines are becoming larger and more complex
The frequency components of the raw vibration signal were calculated to determine whether a resample was needed
In order to detect gearbox faults of wind turbines as early as possible, we proposed a novel fault diagnosis method based on improved ensemble empirical mode decomposition (EEMD) and Hilbert square demodulation (HSD)
Summary
In order to harvest wind energy more efficiently, wind turbines are becoming larger and more complex. Traditional time domain and frequency analysis techniques, such as energy analysis, kurtosis, crest factor and spectrum analysis, have been widely used in fault diagnosis, these methods have only been effective in a stationary signal process. When it comes to non-stationary signal analyzing, the diagnostic performance has usually been unsatisfactory. The modulating signal results from the impacts caused by defects of a bearing or gear impulses appearing every time the tooth or rolling element crosses the defected area, which leads to amplitude modulation [20,21] To deal with this phenomenon, Hilbert square demodulation (HSD) techniques are introduced. Review of Ensemble Empirical Mode Decomposition and Hilbert Square Demodulation
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