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A Hybrid Degradation Evaluation Model for Aero-Engines

Author

Listed:
  • Likun Ren

    (Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

  • Haiqin Qin

    (Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

  • Na Cai

    (Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

  • Bianjiang Li

    (Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

  • Zhenbo Xie

    (Qingdao Campus, Naval Aviation University, Qingdao 266041, China)

Abstract

The non-convergence and low efficiency of the thermodynamic model make them difficult to be used in the aero-engines degradation evaluation, while the negligence of the thermodynamics process of data-driven degradation evaluation methods makes them inaccurate and hard to analyze the actual degradation of air path components. So, we propose a thermodynamic-based and data-driven hybrid model for aero-engine degradation evaluation. Different from thermodynamic-based methods, the iteration calculation is converted to the forward flow in the proposed neural network, thus improving convergence. Moreover, a multi-objective loss function considering the components co-operation process and fusion training process fully taking advantage of simulation and degradation trajectory datasets are proposed to improve the degradation evaluation accuracy. The test case is carried out on NASA’s benchmark for aero-engine degradation evaluation. The result shows that the proposed method can improve the accuracy significantly, which suggests its effectiveness.

Suggested Citation

  • Likun Ren & Haiqin Qin & Na Cai & Bianjiang Li & Zhenbo Xie, 2022. "A Hybrid Degradation Evaluation Model for Aero-Engines," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:29-:d:1008927
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    References listed on IDEAS

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    1. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
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