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Gas path deterioration observation based on stochastic dynamics for reliability assessment of aeroengines

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  • Huang, Dawen
  • Zhou, Dengji
  • Wei, Xunkai
  • Wang, Hao
  • Zhao, Xuehong

Abstract

Gas path deterioration seriously reduces the reliability of aeroengine systems. Accurate observation of gas path deterioration parameters is the key to ensuring the reliable operation and safety control of aeroengines. It is a significant challenge to accurately observe the deterioration state under multi-source uncertainties. This work provides a novel assessment technique to accurately master the gas path deterioration from the perspective of stochastic dynamics for the first time, in contrast to the previous assessment schemes based on the aerothermodynamics model and filtering theory. Based on the state parameters and stochastic optimal inputs, a full-dimensional performance observation equation is created. The construction of the stochastic optimal inputs ensures the tracking of the gas path deteriorating state through the negative feedback of state parameters and the rectification of measurement data. The observation principle and algorithm are also thoroughly presented. Three common deterioration modes serve as proof of the proposed method's effectiveness. The evaluation accuracy is increased by at least 22.75% when compared to the conventional method, and the maximum improvement rate reaches 52.45%. The proposed method is free from multi-source uncertainty interference and has higher accuracy and robustness. It offers a fresh approach to master gas path deterioration and ensures safe and reliable operation.

Suggested Citation

  • Huang, Dawen & Zhou, Dengji & Wei, Xunkai & Wang, Hao & Zhao, Xuehong, 2023. "Gas path deterioration observation based on stochastic dynamics for reliability assessment of aeroengines," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003721
    DOI: 10.1016/j.ress.2023.109458
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    Cited by:

    1. Liao, Zengbu & Zhan, Keyi & Zhao, Hang & Deng, Yuntao & Geng, Jia & Chen, Xuefeng & Song, Zhiping, 2024. "Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    2. Gan, Chenyu & Ding, Shuiting & Qiu, Tian & Liu, Peng & Ma, Qinglin, 2024. "Model-based safety analysis with time resolution (MBSA-TR) method for complex aerothermal–mechanical systems of aero-engines," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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