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Open AccessArticle
Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach
by
Jihui Xu
Jihui Xu 1,
Lu Chen
Lu Chen 1,*,
Huaixi Xing
Huaixi Xing 2 and
Wenjie Tian
Wenjie Tian 1
1
Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi’an 710051, China
2
Air and Missile Defence College, Air Force Engineering University, Xi’an 710051, China
*
Author to whom correspondence should be addressed.
Submission received: 4 June 2024
/
Revised: 25 July 2024
/
Accepted: 2 August 2024
/
Published: 6 August 2024
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This work expresses the correlation among accident causal factors, flight phases, accident types, and consequences. It identifies the potential risks of various accident causal factors to help eliminate risk hazards and reduce the probability of aviation accidents, providing auxiliary decision making and a practical reference for preventing aviation accidents.
Abstract
Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.
Share and Cite
MDPI and ACS Style
Xu, J.; Chen, L.; Xing, H.; Tian, W.
Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach. Appl. Sci. 2024, 14, 6887.
https://doi.org/10.3390/app14166887
AMA Style
Xu J, Chen L, Xing H, Tian W.
Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach. Applied Sciences. 2024; 14(16):6887.
https://doi.org/10.3390/app14166887
Chicago/Turabian Style
Xu, Jihui, Lu Chen, Huaixi Xing, and Wenjie Tian.
2024. "Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach" Applied Sciences 14, no. 16: 6887.
https://doi.org/10.3390/app14166887
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