IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v127y2018icp452-460.html
   My bibliography  Save this article

A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process

Author

Listed:
  • Hu, Yaogang
  • Li, Hui
  • Shi, Pingping
  • Chai, Zhaosen
  • Wang, Kun
  • Xie, Xiangjie
  • Chen, Zhe

Abstract

A performance degradation model and a real-time remaining useful life (RUL) prediction method are proposed on the basis of temperature characteristic parameters to determine the RUL of wind turbine bearings. First, using the moving average method, the relative temperature data of wind turbine bearings are smoothed, and the temperature trend data are obtained on the basis of the uncertainty of wind speed and wind direction that causes the temperature of wind turbine bearings to vary widely. Second, given that the degradation speed of bearings changes with operational time and uncertain external factors, the performance degradation model is established with the Wiener process. The parameters of this model are obtained through the maximum likelihood estimation method. Third, according to the failure principle of the first temperature monitoring value beyond the first warning threshold, the RUL prediction model for wind turbine bearings is established on the basis of an inverse Gaussian distribution. Finally, the performance degradation process and real-time RUL prediction are demonstrated by predicting the RUL of a practical rear bearing of a wind turbine generator. The comparison of the predicted RUL and actual RUL shows that the proposed model and prediction method are correct and effective.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:127:y:2018:i:c:p:452-460
    DOI: 10.1016/j.renene.2018.04.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118304415
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2018.04.033?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. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
    2. Sheng‐Tsaing Tseng & Jen Tang & In‐Hong Ku, 2003. "Determination of burn‐in parameters and residual life for highly reliable products," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(1), pages 1-14, February.
    3. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.
    4. 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.
    5. 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.
    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. Chen, Jinglong & Jing, Hongjie & Chang, Yuanhong & Liu, Qian, 2019. "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 372-382.
    2. Jin Cui & Licai Cao & Tianxiao Zhang, 2024. "A two-stage Gaussian process regression model for remaining useful prediction of bearings," Journal of Risk and Reliability, , vol. 238(2), pages 333-348, April.
    3. Jiang, Deyin & Chen, Tianyu & Xie, Juanzhang & Cui, Weimin & Song, Bifeng, 2023. "A mechanical system reliability degradation analysis and remaining life estimation method——With the example of an aircraft hatch lock mechanism," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    5. Tian, Zhongda & Chen, Hao, 2021. "Multi-step short-term wind speed prediction based on integrated multi-model fusion," Applied Energy, Elsevier, vol. 298(C).
    6. Rommel, D.P. & Di Maio, D. & Tinga, T., 2020. "Calculating wind turbine component loads for improved life prediction," Renewable Energy, Elsevier, vol. 146(C), pages 223-241.
    7. Liu, Shujie & Fan, Lexian, 2022. "An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    8. 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.
    9. 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.
    10. Zheng Wang & Peng Gao & Xuening Chu, 2022. "Remaining Useful Life Prediction of Wind Turbine Gearbox Bearings with Limited Samples Based on Prior Knowledge and PI-LSTM," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    11. Ren, He & Liu, Wenyi & Shan, Mengchen & Wang, Xin & Wang, Zhengfeng, 2021. "A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation," Renewable Energy, Elsevier, vol. 168(C), pages 972-980.

    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. 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.
    2. 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.
    3. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
    4. 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.
    5. 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.
    6. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
    7. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Ken Bruton & Dominic T. J. O’Sullivan, 2018. "A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study," Energies, MDPI, vol. 11(7), pages 1-21, July.
    8. Mérigaud, Alexis & Ringwood, John V., 2016. "Condition-based maintenance methods for marine renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 53-78.
    9. 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.
    10. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
    11. Xiangang Cao & Pengfei Li & Song Ming, 2021. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    12. Moon, Yongma & Baran, Mesut, 2018. "Economic analysis of a residential PV system from the timing perspective: A real option model," Renewable Energy, Elsevier, vol. 125(C), pages 783-795.
    13. Tianyu Liu & Zhengqiang Pan & Quan Sun & Jing Feng & Yanzhen Tang, 2017. "Residual useful life estimation for products with two performance characteristics based on a bivariate Wiener process," Journal of Risk and Reliability, , vol. 231(1), pages 69-80, February.
    14. Jianxun Zhang & Xiao He & Xiaosheng Si & Changhua Hu & Donghua Zhou, 2017. "A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena," Energies, MDPI, vol. 10(11), pages 1-24, October.
    15. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.
    16. Zhou, Shirong & Tang, Yincai & Xu, Ancha, 2021. "A generalized Wiener process with dependent degradation rate and volatility and time-varying mean-to-variance ratio," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    17. 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.
    18. Moynihan, Bridget & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2022. "Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification," Renewable Energy, Elsevier, vol. 184(C), pages 662-676.
    19. 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.
    20. Xiaodong Xu & Chuanqiang Yu & Shengjin Tang & Xiaoyan Sun & Xiaosheng Si & Lifeng Wu, 2019. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect," Energies, MDPI, vol. 12(9), pages 1-17, May.

    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:127:y:2018:i:c:p:452-460. 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.