IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v92y2016icp104-116.html
   My bibliography  Save this article

Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion

Author

Listed:
  • Yang, Dong
  • Li, Hui
  • Hu, Yaogang
  • Zhao, Jie
  • Xiao, Hongwei
  • Lan, Yongsen

Abstract

A noise suppression method for feature frequency extraction that is supplemented with multi-point data fusion was investigated in consideration of issues involving wind turbine vibration signals subject to high noise disturbance. The difficulty of extracting early weak fault features was examined as well. First, a de-noising and feature extraction method that uses EMD-Correlation was developed by adding empirical mode decomposition (EMD) and autocorrelation de-noising to the wavelet package transform under the effects of white noise and short-term disturbance noise in wind turbine vibration signals. Second, an EMD-Correlation analysis model for feature frequency extraction supplemented with multi-point data fusion was established with reference to adaptive resonance theory-2 to highlight the feature frequency of a possible early weak fault. Third, the results obtained with the actual and simulated fault vibration signals of wind turbine bearing faults and the outcomes of comparing the different feature frequency extraction methods show that the proposed method of EMD-Correlation that is supplemented with multi-point data fusion can not only effectively reduce white noise and short-term disturbance noise but can also extract the feature frequency of early weak faults. Finally, prototype hardware and software were developed for a wind turbine condition monitoring system based on the aforementioned fault feature extraction algorithms and tested in an actual wind turbine generation system.

Suggested Citation

  • Yang, Dong & Li, Hui & Hu, Yaogang & Zhao, Jie & Xiao, Hongwei & Lan, Yongsen, 2016. "Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion," Renewable Energy, Elsevier, vol. 92(C), pages 104-116.
    • Handle: RePEc:eee:renene:v:92:y:2016:i:c:p:104-116
    DOI: 10.1016/j.renene.2016.01.099
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116300994
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.01.099?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. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
    2. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    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. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.
    2. Papatheou, Evangelos & Dervilis, Nikolaos & Maguire, Andrew E. & Campos, Carles & Antoniadou, Ifigeneia & Worden, Keith, 2017. "Performance monitoring of a wind turbine using extreme function theory," Renewable Energy, Elsevier, vol. 113(C), pages 1490-1502.
    3. 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.
    4. Hu, Yaogang & Li, Hui & Shi, Pingping & Chai, Zhaosen & Wang, Kun & Xie, Xiangjie & Chen, Zhe, 2018. "A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process," Renewable Energy, Elsevier, vol. 127(C), pages 452-460.
    5. Pan, Yubin & Hong, Rongjing & Chen, Jie & Wu, Weiwei, 2020. "A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox," Renewable Energy, Elsevier, vol. 152(C), pages 138-154.
    6. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
    7. Liu, W.Y., 2017. "A review on wind turbine noise mechanism and de-noising techniques," Renewable Energy, Elsevier, vol. 108(C), pages 311-320.
    8. Lei Fu & Tiantian Zhu & Kai Zhu & Yiling Yang, 2019. "Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy," Energies, MDPI, vol. 12(16), pages 1-20, August.

    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. 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.
    2. Peng Guo & Nan Bai, 2011. "Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods," Energies, MDPI, vol. 4(11), pages 1-17, November.
    3. Wakui, Tetsuya & Yokoyama, Ryohei, 2013. "Wind speed sensorless performance monitoring based on operating behavior for stand-alone vertical axis wind turbine," Renewable Energy, Elsevier, vol. 53(C), pages 49-59.
    4. Papini, Guglielmo & Faedo, Nicolás & Mattiazzo, Giuliana, 2024. "Fault diagnosis and fault-tolerant control in wave energy: A perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    5. Faiz, Jawad & Moosavi, S.M.M., 2016. "Eccentricity fault detection – From induction machines to DFIG—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 169-179.
    6. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    7. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    8. Yang, Wenxian & Tian, Sunny W., 2015. "Research on a power quality monitoring technique for individual wind turbines," Renewable Energy, Elsevier, vol. 75(C), pages 187-198.
    9. Kandukuri, Surya Teja & Klausen, Andreas & Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar, 2016. "A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 697-708.
    10. Dao, Phong B., 2022. "Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data," Renewable Energy, Elsevier, vol. 185(C), pages 641-654.
    11. 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.
    12. Wymore, Mathew L. & Van Dam, Jeremy E. & Ceylan, Halil & Qiao, Daji, 2015. "A survey of health monitoring systems for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 976-990.
    13. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    14. 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.
    15. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    16. Fanghong Zhang & Mingsong Chen & Yuze Zhu & Kai Zhang & Qingan Li, 2023. "A Review of Fault Diagnosis, Status Prediction, and Evaluation Technology for Wind Turbines," Energies, MDPI, vol. 16(3), pages 1-15, January.
    17. 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.
    18. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    19. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.
    20. 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.

    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:92:y:2016:i:c:p:104-116. 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.