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A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss

Author

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  • Zhang, Xinhai
  • Wang, Kang
  • Geng, Jia
  • Li, Ming
  • Song, Zhiping

Abstract

The acceleration performance of turbofan engine is mainly limited by the surge boundary of high-pressure compressor (HPC). A certain amount of surge margin (SM) is reserved between the boundary and the acceleration schedule (AS) of acceleration control to avoid the risk caused by intake distortion, etc. However, the performance improvement by low SM design is the requirement for advanced engines. Hence it must execute a fault-tolerant strategy for the impact of sensor error, fuel metering error, etc. This paper proposes a method by adopting four ASs for weighted average to replace the single AS. The weights are altered by the multi-layer perceptron with reference to the real-time fuel results from four ASs, intake condition and rotor speed. For safety and accuracy, an exponential Gumbel loss function is introduced into the model training. The simulation results demonstrate that the method can significantly tolerate the impact of a single fault and the normal deviations of other factors without causing SM of HPC to be less than 3.6%. These also indicate that the acceleration time on the ground does not exceed acceptable 5 s, with a median that is 0.34 s less than the minimum method of two ASs within the flight envelope.

Suggested Citation

  • Zhang, Xinhai & Wang, Kang & Geng, Jia & Li, Ming & Song, Zhiping, 2024. "A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006455
    DOI: 10.1016/j.energy.2024.130873
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    References listed on IDEAS

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    1. Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
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    Cited by:

    1. Zhao, Hang & Zhai, Xiongfei & Liao, Zengbu & Li, Zichen & Song, Zhiping, 2025. "Gas turbine thrust estimation in sensor drift scenarios using a three-stage multi-target domain adaptation method," Energy, Elsevier, vol. 314(C).
    2. Xue, Qi & Jin, Xinqiao & Jia, Zhiyang & Lyu, Yuan & Du, Zhimin, 2024. "Optimal control strategy of multiple chiller system based on background knowledge graph," Applied Energy, Elsevier, vol. 375(C).
    3. Wang, Kang & Zhang, Xinhai & Feng, Hailong & Li, Ming & Liu, Jinxin & Song, Zhiping, 2025. "Hybrid acceleration schedule design for gas turbine engine using adaptive sample error weighting multilayer perceptron network," Energy, Elsevier, vol. 318(C).

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