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Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines

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  • Li, Xilin
  • Teng, Wei
  • Peng, Dikang
  • Ma, Tao
  • Wu, Xin
  • Liu, Yibing

Abstract

Accurate remaining useful life prognosis of bearings in wind turbines is beneficial for operation and maintenance schedule at wind farms. The major technical challenges include how to construct the strictly monotonic and gently growing health indicators of bearings, and further improve the prediction performance under harsh operational environments. To deal with these challenges, this paper proposes a degraded feature fusion model aiming at the construction of monotonic health indicators. In the model, the features with higher monotonicity are selected as sensitive features, and used to calculate the degradation ratios for health indicators. A generalizable failure threshold determination method is presented to find the common characteristics of failure patterns of a set of indicators. Besides, a novel state-space estimator with self-constraint property is proposed, which can update the state-space in the future time for more robust remaining useful life prediction. Several sets of the bearings from PRONOSTIA platform and on-site wind turbine high-speed shafts are utilized to validate the proposed approach, the former results demonstrate that the prediction accuracy of the proposed approach is better than some existing algorithms, and the latter shows that the potential of the proposed approach to provide advices for maintenance schedule.

Suggested Citation

  • Li, Xilin & Teng, Wei & Peng, Dikang & Ma, Tao & Wu, Xin & Liu, Yibing, 2023. "Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:reensy:v:233:y:2023:i:c:s095183202300039x
    DOI: 10.1016/j.ress.2023.109124
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    References listed on IDEAS

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    1. Wang, Han & Liao, Haitao & Ma, Xiaobing & Bao, Rui, 2021. "Remaining Useful Life Prediction and Optimal Maintenance Time Determination for a Single Unit Using Isotonic Regression and Gamma Process Model," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    2. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    3. Xiao, Lei & Tang, Junxuan & Zhang, Xinghui & Bechhoefer, Eric & Ding, Siyi, 2021. "Remaining useful life prediction based on intentional noise injection and feature reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Baptista, Marcia & Henriques, Elsa M.P. & de Medeiros, Ivo P. & Malere, Joao P. & Nascimento, Cairo L. & Prendinger, Helmut, 2019. "Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 228-239.
    6. Yan, Tao & Lei, Yaguo & Li, Naipeng & Wang, Biao & Wang, Wenting, 2021. "Degradation modeling and remaining useful life prediction for dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
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    Cited by:

    1. Zhu, Dongping & Huang, Xiaogang & Ding, Zhixia & Zhang, Wei, 2024. "Estimation of wind turbine responses with attention-based neural network incorporating environmental uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Sarah Barber & Unai Izagirre & Oscar Serradilla & Jon Olaizola & Ekhi Zugasti & Jose Ignacio Aizpurua & Ali Eftekhari Milani & Frank Sehnke & Yoshiaki Sakagami & Charles Henderson, 2023. "Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation," Energies, MDPI, vol. 16(8), pages 1-23, April.
    3. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Wu, Bin & Zhang, Xiaohong & Shi, Hui & Zeng, Jianchao, 2024. "Failure mode division and remaining useful life prognostics of multi-indicator systems with multi-fault," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Park, Hyung Jun & Kim, Nam H. & Choi, Joo-Ho, 2024. "A robust health prediction using Bayesian approach guided by physical constraints," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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