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A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox

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  • Pan, Yubin
  • Hong, Rongjing
  • Chen, Jie
  • Wu, Weiwei

Abstract

Gearbox is one of critical transmission components in wind turbine (WT) having a high downtime rate among all subcomponents. Fault prognostics and health management (PHM) of WT gearbox is crucial to their high reliability operation. However, presence of background noise in WT signals restricts the applicability of existing PHM approaches in feature extraction. To solve this problem, a novel performance degradation assessment method based on deep belief network (DBN) and self-organizing map (SOM) is proposed to de-noise and merge multi-sensor vibration signals. Minimum quantization error (MQE) is defined as health indicator to detect incipient fault of WT gearbox. After health indicator construction, an improved particle filtering (PF) optimized by fruit fly optimization algorithm (FOA) is employed to predict the remaining use life (RUL) of WT gearbox. To take advantage of dynamic and random operation process of WT gearbox, Wiener-process-based degradation model is developed to improving RUL prediction efficiency. The effectiveness is validated by using simulated as well as experimental vibration signals obtained through a WT gearbox highly accelerated life test. The results illustrate that proposed method can evaluate performance degradation process and predict RUL of WT gearbox effectively.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:152:y:2020:i:c:p:138-154
    DOI: 10.1016/j.renene.2020.01.042
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    Cited by:

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    3. Hou, Guolian & Wang, Junjie & Fan, Yuzhen, 2024. "Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction," Energy, Elsevier, vol. 286(C).
    4. Guo, Junyu & Wan, Jia-Lun & Yang, Yan & Dai, Le & Tang, Aimin & Huang, Bangkui & Zhang, Fangfang & Li, He, 2023. "A deep feature learning method for remaining useful life prediction of drilling pumps," Energy, Elsevier, vol. 282(C).
    5. Mingzhu Tang & Zixin Liang & Huawei Wu & Zimin Wang, 2021. "Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF," Energies, MDPI, vol. 14(19), pages 1-13, October.
    6. Zhang, Yagang & Kong, Xue & Wang, Jingchao & Wang, Hui & Cheng, Xiaodan, 2024. "Wind power forecasting system with data enhancement and algorithm improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    7. Ding, Yifei & Jia, Minping & Miao, Qiuhua & Huang, Peng, 2021. "Remaining useful life estimation using deep metric transfer learning for kernel regression," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    8. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
    9. Qiwu Zhu & Qingyu Xiong & Zhengyi Yang & Yang Yu, 2023. "A novel feature-fusion-based end-to-end approach for remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3495-3505, December.

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