Fault feature extraction of a wind turbine gearbox using adaptive parameterless empirical wavelet transform

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (8) : 99-105.

DING Xian1，XU Jin1，TENG Wei2，WANG Wei2

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Abstract

Wind turbines operate as an equipment cluster, bringing massive vibration signals due to their complex structures and multiply vibration measures.Only analysing the vibration signals to detect fault by human is challenging.In this paper, a fault feature extraction method for wind turbine gearboxes was proposed on the basis of the parameterless empirical wavelet transform.The scale space method and empirical law were utilized to automatically split the Fourier spectrum of the vibration signal, and different frequency bands were obtained.A series of empirical wavelet filters were designed based on the split frequency bands to decompose the signal into multiply empirical modes.The metric of margin factor was adopted to sort the empirical modes, and the empirical mode with maximum margin factor was recognized as the most sensitive one to fault.The proposed method is adaptive without any presented parameters.The fault signals from an experimental platform and a real wind turbine gearbox verified the proposed method.

Key words

parameterless / empirical wavelet transform / margin factor / adaptive / fault feature extraction

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DING Xian1，XU Jin1，TENG Wei2，WANG Wei2. Fault feature extraction of a wind turbine gearbox using adaptive parameterless empirical wavelet transform[J]. Journal of Vibration and Shock, 2020, 39(8): 99-105

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