基于平均场理论的航空安全风险预警模型

doi:10.12305/j.issn.1001-506X.2025.01.22

Abstract

Abstract:

As the occurrence of aviation accidents is often difficult to predict and irreversible, how to develop effective aviation warning plans is the key to avoiding the occurrence of accidents. Aiming at aviation safety risk early warning, the aviation risk propagation dynamics model is introduced and the probability of aviation system accidents triggering based on the mean field theory is derived. Through sensitivity analysis, the increase rate of triggering events on aviation accidents is obtained, and a pre-accident warning model is proposed. The results show that more resources should be allocated to trigger events such as poor visibility, aircraft system failure, fatigue, poor handling of special situation, insufficient skills and insufficient maintenance of equipment in the pre-warning model to avoid the occurrence of the above events. Based on the risk transmission law of aviation accidents, the early warning model of aviation accidents in the event is established, the risk evolution event set affected by triggering events is obtained, and the corresponding sequence is given so that managers can effectively allocate resources.

Key words: aviation accident report, complex network, epidemic model, mean field theory, risk early warning

CLC Number:

V328

Han ZHANG, Qiang WANG, Guilong MIN. Aviation safety risk early warning model based on mean field theory[J]. Systems Engineering and Electronics, 2025, 47(1): 210-216.

Figures/Tables 5

References 30

1	LEEW K,KIMS J.Roles of safety management system (SMS) in aircraft development[J].International Journal of Aeronautical and Space Sciences,2015,16(3):451-462. doi: 10.5139/IJASS.2015.16.3.451
2	ZHOUD,ZHUANGX,ZUOH F,et al.A model fusion stra- tegy for identifying aircraft risk using CNN and Att-BiLSTM[J].Reliability Engineering & System Safety,2022,228,108750.
3	WALKERG.Redefining the incidents to learn from: safety science insights acquired on the journey from black boxes to flight data monitoring[J].Safety Science,2017,99,14-22. doi: 10.1016/j.ssci.2017.05.010
4	TUEGELE J,INGRAFFEAA R,EASONT G,et al.Reengineering aircraft structural life prediction using a digital twin[J].International Journal of Aerospace Engineering,2011,2021(1):154798.
5	ZHUS P,HUANGH Z,PENGW,et al.Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty[J].Reliability Engineering & System Safety,2016,146,1-12.
6	DENGK Y,ZHANGX Y,CHENGY J,et al.A remaining useful life prediction method with long-short term feature processing for aircraft engines[J].Applied Soft Computing,2020,93(2):106344-106357.
7	GASTERENH V,KRIJGSVELDK L,KLAUKEN,et al.Aeroecology meets aviation safety: early warning systems in Europe and the Middle East prevent collisions between birds and aircraft[J].Ecography,2019,42(5):899-911. doi: 10.1111/ecog.04125
8	XUB,WUQ,XIC,et al.Recognition of the fatigue status of pilots using BF-PSO optimized multi-class GP classification with sEMG signals[J].Reliability Engineering & System Safety,2020,199,106930.
9	ZHANG Y T, CHEN Y Q, CHEN S. Based on aviation safety and flight technology automation early warning research[C]//Proc. of the IEEE International Conference on Sensors, Electronics and Computer Engineering, 2023: 723-728.
10	ZHANGY J,WANGB T,NINGY,et al.Study on health monitoring and fatigue life prediction of aircraft structures[J].Materials,2022,15(23):8606-8617. doi: 10.3390/ma15238606
11	SUNJ Y,LIAOY,LUF,et al.Assessment of pilot fatigue risk on international flights under the prevention and control policy of the Chinese civil aviation industry during the COVID-19[J].Journal of Air Transport Management,2023,112,102466. doi: 10.1016/j.jairtraman.2023.102466
12	OSTERC V,STRONGJ S,ZORNC K.Analyzing aviation safety: problems, challenges, opportunities[J].Research in Transportation Economics,2013,43(1):148-164. doi: 10.1016/j.retrec.2012.12.001
13	JANICM.An assessment of risk and safety in civil aviation[J].Journal of Air Transport Management,2000,6(1):43-50. doi: 10.1016/S0969-6997(99)00021-6
14	STROEVES H,SOMP,VANB A,et al.Strengthening air traffic safety management by moving from outcome-based towards risk-based evaluation of runway incursions[J].Reliability Engineering & System Safety,2016,147(3):93-108.
15	LIUY,WANGW J,HUANGH Z,et al.A new simulation model for assessing aircraft emergency evacuation considering passenger physical characteristics[J].Reliability Engineering & System Safety,2014,121(8):187-197.
16	REYM,ALOISED,SOUMISF,et al.A data-driven model for safety risk identification from flight data analysis[J].Transportation Engineering,2021,5,100087. doi: 10.1016/j.treng.2021.100087
17	CONNELL L J. Aviation safety incident reporting: NASA's aviation safety reporting system[C]//Proc. of the Transportation Research Board Conference, 2000.
18	GAOY,HAOY,WANGS,et al.The dynamics between vo-luntary safety reporting and commercial aviation accidents[J].Safety Science,2021,141,105351. doi: 10.1016/j.ssci.2021.105351
19	SJOBLOM O. Data mining in promoting aviation safety mana-gement[C]//Proc. of the Safe and Secure Cities: 5th International Conference on Well-Being in the Information Society, 2014: 186-193.
20	PASSARELLAR,OKTARIANIG,KURNIAWAND,et al.Using the agglomerative hierarchical clustering method to examine human factors in Indonesian aviation accidents[J].International Journal of Advanced Computer Science and Applications,2022,13(9):325-331.
21	ROSER L,PURANIKT G,MAVRISD N,et al.Application of structural topic modeling to aviation safety data[J].Reliabi-lity Engineering & System Safety,2022,224,108522.
22	MIYAMOTOA,BENDARKARM V,MAVRISD N.Natural language processing of aviation safety reports to identify inefficient operational patterns[J].Aerospace,2022,9(8):450. doi: 10.3390/aerospace9080450
23	DONGT X,YANGQ W,EBADIN,et al.Identifying incident causal factors to improve aviation transportation safety: proposing a deep learning approach[J].Journal of Advanced Transportation,2021,2021(1):5540046.
24	ALKHAMISI A O, MEHMOOD R. An ensemble machine and deep learning model for risk prediction in aviation systems[C]// Proc. of the 6th Conference on Data Science and Machine Learning Applications, 2020: 54-59.
25	MAX X,DENGW Y,QIAOW L,et al.A methodology to quantify the risk propagation of hazardous events for ship grounding accidents based on directed CN[J].Reliability Engineering & System Safety,2022,221,108334.
26	张晗,王强.基于有向网络的航空安全事故风险识别与评估[J].系统工程与电子技术,2024,46(6):1995-2011.
	ZHANGH,WANGQ.Aviation safety accident risk identification and evaluation based on directed networks[J].Systems Engineering and Electronics,2024,46(6):1995-2011.
27	LVC C,YUANZ W,SIS B,et al.Cascading failure in networks with dynamical behavior against multi-node removal[J].Chaos, Solitons & Fractals,2022,160,112270.
28	NEWMANM E J.Spread of epidemic disease on networks[J].Physical Review E,2002,66(1):016128.
29	LIUF,LIX,ZHUG F.Using the contact network model and Metropolis-Hastings sampling to reconstruct the COVID-19 spread on the "Diamond Princess"[J].Science Bulletin,2020,65(15):1297-1305.
30	CHENM,LIM,HAOY X,et al.The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy[J].Information Fusion,2020,64,252-258.

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Aviation safety risk early warning model based on mean field theory

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