Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement
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 hea...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| author | Weidong He Xiaojing Yin Yubo Shao Dianxin Chen Jianglong Mi Yang Jiao |
| author_facet | Weidong He Xiaojing Yin Yubo Shao Dianxin Chen Jianglong Mi Yang Jiao |
| author_sort | Weidong He |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-894d62a6694444ebab6b78c0bd8dd665 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-894d62a6694444ebab6b78c0bd8dd6652025-01-10T13:18:19ZengMDPI AGMathematics2227-73902024-12-0113112310.3390/math13010123Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network ImprovementWeidong He0Xiaojing Yin1Yubo Shao2Dianxin Chen3Jianglong Mi4Yang Jiao5The School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, ChinaThe School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, ChinaThe School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, ChinaThe School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, ChinaThe School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, ChinaAviation Foundation Institute, Air Force Aviation University, Changchun 130012, ChinaAs 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.https://www.mdpi.com/2227-7390/13/1/123nutcracker optimizer algorithmgraph neural networkfault tree analysisaviation fuel pumpstechnique for order of preference by similarity to ideal solution |
| spellingShingle | Weidong He Xiaojing Yin Yubo Shao Dianxin Chen Jianglong Mi Yang Jiao Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement Mathematics nutcracker optimizer algorithm graph neural network fault tree analysis aviation fuel pumps technique for order of preference by similarity to ideal solution |
| title | Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement |
| title_full | Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement |
| title_fullStr | Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement |
| title_full_unstemmed | Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement |
| title_short | Fuzzy Fault Tree Maintenance Decision Analysis for Aviation Fuel Pumps Based on Nutcracker Optimization Algorithm–Graph Neural Network Improvement |
| title_sort | fuzzy fault tree maintenance decision analysis for aviation fuel pumps based on nutcracker optimization algorithm graph neural network improvement |
| topic | nutcracker optimizer algorithm graph neural network fault tree analysis aviation fuel pumps technique for order of preference by similarity to ideal solution |
| url | https://www.mdpi.com/2227-7390/13/1/123 |
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