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Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum

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Abstract

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.

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Acknowledgements

The authors gratefully appreciate all the reviewers and the editor for their valuable comments and advices about our manuscript. The authors gratefully acknowledge the support of this research work by the National Natural Science Foundation of China (Grant No. 51335006).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Yun Kong, Tianyang Wang, Zheng Li & Fulei Chu

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  1. Yun Kong
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  2. Tianyang Wang
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  3. Zheng Li
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  4. Fulei Chu
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Correspondence to Tianyang Wang.

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Kong, Y., Wang, T., Li, Z. et al. Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum. Front. Mech. Eng. 12, 406–419 (2017). https://doi.org/10.1007/s11465-017-0419-0

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  • DOI: https://doi.org/10.1007/s11465-017-0419-0

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