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Simulation model and fault analysis of air circulation system of the aircraft based on grasshopper optimization algorithm: support vector machine

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Abstract

To effectively analyze the working state of the air circulation system of the aircraft at high altitude, it is necessary to conduct simulation analysis on the ground. In this paper, a simulated annealing-grasshopper optimization algorithmtion algorithm support vector machine is proposed to establish the overall simulation model of the circulation system of the aircraft and to conduct fault injection analysis. By introducing the support vector machine to classify the results of the system and applying of grasshopper algorithm to optimize the support vector machine with methods such as simulated annealing and position migration, the optimal parameter values can be obtained. The results indicate that the simulation system can effectively simulate the temperature changes of the aircraft in various operating states; the optimized support vector machine can effectively distinguish the fault types of the aircraft component outlet; meanwhile, the system convergence is accelerated to avoid falling into the local optimal problems.

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Funding

This study was funded by Shenyang University of Chemical Technology (201601198).

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Authors and Affiliations

  1. College of Science, Shenyang University of Chemical Technology, Shenyang, 110142, Liaoning, China

    Wu Huiyong & Jin Shuchun

  2. Neusoft Corporation, Shenyang, 110179, Liaoning, China

    Jin Zhu

Authors
  1. Wu Huiyong
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  2. Jin Shuchun
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  3. Jin Zhu
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Corresponding author

Correspondence to Jin Shuchun.

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Author WU Huiyong declares that he has no conflict of interest. Author JIN Shuchun declares that he has no conflict of interest. Author JIN Zhu declares that he has no conflict of interest.

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Communicated by Oscar Castillo.

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Huiyong, W., Shuchun, J. & Zhu, J. Simulation model and fault analysis of air circulation system of the aircraft based on grasshopper optimization algorithm: support vector machine. Soft Comput 27, 13269–13284 (2023). https://doi.org/10.1007/s00500-022-07403-2

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