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- 저자 : Elias Hadjadj Aoul (검색결과 1 건)
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Naima Grabsia,Elias Hadjadj Aoul,Salah Saad 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.2
Serious failures of wind turbine drive-trains occur in gear which plays an essential role. Owing to the complicated vibration signal of faulty gear and the characteristic fault frequency buried in the background noise. Thus, detecting a defect of this component with classical methods is a great challenge. In order to overcome this issue, a combined technique of time–frequency analysis based on Morlet wavelet coeffi cient (MWC) and fast kurtogram (MWC-FK) is proposed for gear fault detection. The Morlet wavelet (MW) is able to detect components impulses and the fast Kurtogram (FK) is appropriate for environmental noise elimination and extracts the impulses in the fi ltered signal. First, the wavelet coeffi cient is obtained using the continuous Morlet wavelet transform decomposition for further analysis. Then, the wavelet coeffi cient signal that has the highest value of the kurtosis index is chosen. Finally, the selected signal is fi ltered by an optimal band-pass fi lter based on fast kurtogram. In order to confi rm the usefulness and robustness of the proposed method, a real vibration signal of wind-turbine pinion with fault is used in this work. The results have showed the effi ciency of the proposed method in gear fault detection and the extraction of fault characteristic frequencies by the squared envelope spectrum (SES) of the fi ltered Morlet wavelet coeffi cient signal.
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