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Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement

Author

Listed:
  • Weidong He

    (The School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China)

  • Xiaojing Yin

    (The School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China
    School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China)

  • Yubo Shao

    (The School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China)

  • Dianxin Chen

    (The School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China)

  • Jianglong Mi

    (The School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China)

  • Yang Jiao

    (Aviation Foundation Institute, Air Force Aviation University, Changchun 130012, China)

Abstract

As a critical component of the engine, the failure of aviation fuel pumps can lead to serious safety accidents, necessitating the development of effective maintenance programs. Fault Tree Analysis (FTA) has a clear structure and strong interpretability in maintenance decision making. However, it heavily relies on expert knowledge, which is subject to uncertainty and incoherence. Therefore, this paper proposes the NOA (Nutcracker Optimization Algorithm)–GNN (Graph Neural Network) model to enhance the accuracy and robustness of FTA by mitigating the uncertainty and inconsistency in expert knowledge. The NOA algorithm efficiently searches the solution space to identify globally optimal solutions. An FTA-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) maintenance decision-making framework has also been developed. By integrating FTA with TOPSIS, the proposed method provides a comprehensive and systematic approach that combines qualitative and quantitative analyses, thereby improving the effectiveness and reliability of maintenance decision making.

Suggested Citation

  • Weidong He & Xiaojing Yin & Yubo Shao & Dianxin Chen & Jianglong Mi & Yang Jiao, 2024. "Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement," Mathematics, MDPI, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:123-:d:1557572
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    References listed on IDEAS

    as
    1. Dilbagh Panchal & Dinesh Kumar, 2017. "Maintenance decision-making for power generating unit in thermal power plant using combined fuzzy AHP-TOPSIS approach," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 29(2), pages 248-272.
    2. Hamzeh Soltanali & Mehdi Khojastehpour & José Torres Farinha & José Edmundo de Almeida e Pais, 2021. "An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing," Energies, MDPI, vol. 14(22), pages 1-21, November.
    3. Kamran Zolfi, 2023. "Gold rush optimizer: A new population-based metaheuristic algorithm," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(1), pages 113-150.
    Full references (including those not matched with items on IDEAS)

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