IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v83y2015icp767-778.html
   My bibliography  Save this article

A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension

Author

Listed:
  • Hu, Aijun
  • Yan, Xiaoan
  • Xiang, Ling

Abstract

In this paper an ensemble intrinsic time-scale decomposition (EITD) method based on the cubic spline interpolation and linear transformation of intrinsic time-scale decomposition (ITD) was proposed, which can restrain the end effect and avoid the signal distortion. Combining ensemble intrinsic time-scale decomposition (EITD) with wavelet packet transform (WPT) and correlation dimension (CD), a novel method for decomposing nonstationary vibration signal and diagnosing wind turbine faults is presented. In implementation of the method, wavelet packet transform is employed to denoise raw vibration signals. Some important influencing factors relating directly to the computational precision of correlation dimension are discussed. The advantage of combining EITD and fractal dimension is that it does recognize the wind turbine gearbox fault types, and can solve the difficulty of recognizing fault conditions when two or more fractal dimensions are close to each other. To verify the effectiveness of the EITD-WPT-CD in detecting the faults, their induced vibrations are collected from high speed shaft gear under normal and faulty conditions through acceleration measurement. The results show that this method is capable of extracting the signal features and identifying the working conditions. The fault diagnosis application in a wind turbine gearbox indicates that the proposed method improved the accuracy of fault diagnosis.

Suggested Citation

  • Hu, Aijun & Yan, Xiaoan & Xiang, Ling, 2015. "A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension," Renewable Energy, Elsevier, vol. 83(C), pages 767-778.
    • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:767-778
    DOI: 10.1016/j.renene.2015.04.063
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115003523
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2015.04.063?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    2. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    3. Jiang, Yonghua & Tang, Baoping & Qin, Yi & Liu, Wenyi, 2011. "Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD," Renewable Energy, Elsevier, vol. 36(8), pages 2146-2153.
    4. Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
    5. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
    2. Miao He & David He & Jae Yoon & Thomas J Nostrand & Junda Zhu & Eric Bechhoefer, 2019. "Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach," Journal of Risk and Reliability, , vol. 233(3), pages 303-316, June.
    3. Francesco Castellani & Marco Buzzoni & Davide Astolfi & Gianluca D’Elia & Giorgio Dalpiaz & Ludovico Terzi, 2017. "Wind Turbine Loads Induced by Terrain and Wakes: An Experimental Study through Vibration Analysis and Computational Fluid Dynamics," Energies, MDPI, vol. 10(11), pages 1-19, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Yu & Lu, Wenxiu & Chu, Fulei, 2017. "Planet gear fault localization for wind turbine gearbox using acoustic emission signals," Renewable Energy, Elsevier, vol. 109(C), pages 449-460.
    2. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    3. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    4. Liu, Xianzeng & Yang, Yuhu & Zhang, Jun, 2018. "Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear," Renewable Energy, Elsevier, vol. 122(C), pages 65-79.
    5. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    6. Xueli An & Dongxiang Jiang, 2014. "Bearing fault diagnosis of wind turbine based on intrinsic time-scale decomposition frequency spectrum," Journal of Risk and Reliability, , vol. 228(6), pages 558-566, December.
    7. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    8. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    9. Li, Yanting & Liu, Shujun & Shu, Lianjie, 2019. "Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data," Renewable Energy, Elsevier, vol. 134(C), pages 357-366.
    10. Wang, Anqi & Qian, Zheng & Pei, Yan & Jing, Bo, 2022. "A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks," Renewable Energy, Elsevier, vol. 185(C), pages 267-279.
    11. He, Guolin & Ding, Kang & Li, Weihua & Jiao, Xintao, 2016. "A novel order tracking method for wind turbine planetary gearbox vibration analysis based on discrete spectrum correction technique," Renewable Energy, Elsevier, vol. 87(P1), pages 364-375.
    12. Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
    13. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    14. Song, Zhe & Zhang, Zijun & Jiang, Yu & Zhu, Jin, 2018. "Wind turbine health state monitoring based on a Bayesian data-driven approach," Renewable Energy, Elsevier, vol. 125(C), pages 172-181.
    15. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Henningsen, Keld, 2015. "Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 144-159.
    16. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    17. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Eiriksson, Egill Thor, 2016. "Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes," Renewable Energy, Elsevier, vol. 91(C), pages 90-106.
    18. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
    19. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2021. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes," Energy, Elsevier, vol. 218(C).
    20. Fan Zhang & Juchuan Dai & Deshun Liu & Linxing Li & Xin Long, 2019. "Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data," Energies, MDPI, vol. 12(3), pages 1-20, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:83:y:2015:i:c:p:767-778. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.