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Multi-stage residual life prediction of aero-engine based on real-time clustering and combined prediction model

Author

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  • Liu, Junqiang
  • Yu, Zhuoqian
  • Zuo, Hongfu
  • Fu, Rongchunxue
  • Feng, Xiaonan

Abstract

Due to mass uncertain issues affecting health status of aero-engines, their degradation process commonly exhibits multi-stage features. Also, key characteristics underlying degradation process cannot be precisely described by traditional remaining useful life (RUL) prediction methods. Thus, for multi-stage RUL prediction, we develop a novel real-time combined approach that effectively explains the weights of each base model. The degradation process is divided into multiple stages through real-time clustering. Then, a combined prediction model including Wiener process, LSTM network, and XGBoost is introduced, which can optimally select multiple models for prediction according to AIC. The convergence and generalization of proposed model are proved. Besides, we adopt R2 and Pearson correlation to analyze model selection, further explaining the weights of every base model. The effectiveness of proposed model is validated by comparing with state-of-art methods available. RMSE results of three-stage and four-stage decrease by 2.72% and 2.69% respectively, compared with results of other models. Especially, RMSE of the stage with lowest value reduces by about 4.27%. From 90% whole life analysis, RE results are reduced by 2.77% (three stages) and 2.75% (four stages). Experimental results show that proposed method enhances prediction accuracy and improves model interpretability.

Suggested Citation

  • Liu, Junqiang & Yu, Zhuoqian & Zuo, Hongfu & Fu, Rongchunxue & Feng, Xiaonan, 2022. "Multi-stage residual life prediction of aero-engine based on real-time clustering and combined prediction model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002642
    DOI: 10.1016/j.ress.2022.108624
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    References listed on IDEAS

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    1. Ahmad, Wasim & Khan, Sheraz Ali & Islam, M M Manjurul & Kim, Jong-Myon, 2019. "A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 67-76.
    2. Zhang, Yong & Xin, Yuqi & Liu, Zhi-wei & Chi, Ming & Ma, Guijun, 2022. "Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Deng, Yingjun & Bucchianico, Alessandro Di & Pechenizkiy, Mykola, 2020. "Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
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    9. Liu, Junqiang & Lei, Fan & Pan, Chunlu & Hu, Dongbin & Zuo, Hongfu, 2021. "Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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    Cited by:

    1. Ta, Yuntian & Li, Yanfeng & Cai, Wenan & Zhang, Qianqian & Wang, Zhijian & Dong, Lei & Du, Wenhua, 2023. "Adaptive staged remaining useful life prediction method based on multi-sensor and multi-feature fusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Chen, Dingliang & Qin, Yi & Qian, Quan & Wang, Yi & Liu, Fuqiang, 2023. "Transfer life prediction of gears by cross-domain health indicator construction and multi-hierarchical long-term memory augmented network," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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