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An Enhanced Empirical Wavelet Transform for Features Extraction from Wind Turbine Condition Monitoring Signals

Author

Listed:
  • Pu Shi

    (School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Wenxian Yang

    (School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
    Hunan Province Cooperative Innovation Centre for Wind Power Equipment and Energy Conversion, Hunan Institute of Engineering, Xiangtan 411104, China)

  • Meiping Sheng

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China)

  • Minqing Wang

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract

Feature extraction from nonlinear and non-stationary (NNS) wind turbine (WT) condition monitoring (CM) signals is challenging. Previously, much effort has been spent to develop advanced signal processing techniques for dealing with CM signals of this kind. The Empirical Wavelet Transform (EWT) is one of the achievements attributed to these efforts. The EWT takes advantage of Empirical Mode Decomposition (EMD) in dealing with NNS signals but is superior to the EMD in mode decomposition and robustness against noise. However, the conventional EWT meets difficulty in properly segmenting the frequency spectrum of the signal, especially when lacking pre-knowledge of the signal. The inappropriate segmentation of the signal spectrum will inevitably lower the accuracy of the EWT result and thus raise the difficulty of WT CM. To address this issue, an enhanced EWT is proposed in this paper by developing a feasible and efficient spectrum segmentation method. The effectiveness of the proposed method has been verified by using the bearing and gearbox CM data that are open to the public for the purpose of research. The experiment has shown that, after adopting the proposed method, it becomes much easier and more reliable to segment the frequency spectrum of the signal. Moreover, benefitting from the correct segmentation of the signal spectrum, the fault-related features of the CM signals are presented more explicitly in the time-frequency map of the enhanced EWT, despite the considerable noise contained in the signal and the shortage of pre-knowledge about the machine being investigated.

Suggested Citation

  • Pu Shi & Wenxian Yang & Meiping Sheng & Minqing Wang, 2017. "An Enhanced Empirical Wavelet Transform for Features Extraction from Wind Turbine Condition Monitoring Signals," Energies, MDPI, vol. 10(7), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:972-:d:104347
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    References listed on IDEAS

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    1. Yang, Wenxian & Little, Christian & Court, Richard, 2014. "S-Transform and its contribution to wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 62(C), pages 137-146.
    2. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
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    Cited by:

    1. Francesco Castellani & Luigi Garibaldi & Alessandro Paolo Daga & Davide Astolfi & Francesco Natili, 2020. "Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements," Energies, MDPI, vol. 13(6), pages 1-18, March.
    2. David Watling & Patrícia Baptista & Gonçalo Duarte & Jianbing Gao & Haibo Chen, 2022. "Systematic Method for Developing Reference Driving Cycles Appropriate to Electric L-Category Vehicles," Energies, MDPI, vol. 15(9), pages 1-28, May.

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