考虑多元耦合交互作用的飞机硬着陆风险综合评价方法

doi:10.12305/j.issn.1001-506X.2025.04.23

Abstract

Abstract:

To effectively evaluate the safety risk of aircraft hard landing, which is a typical unfavorable flight state, a comprehensive evaluation method of aircraft hard landing risk considering multiple coupling interaction is proposed in this paper. Correlation analysis is carried out on the preprocessed quick access recorder (QAR) data, and six flight parameters, such as altitude rate, vertical acceleration, angle of attack, etc., are selected as the hard landing risk research factors, and the landing risk coefficient calculation method is proposed by analyzing the data characteristics of the visualization curves. Trapezoidal fuzzy decision-making trial and evaluation laboratory (DEMATEL) method, which integrates the subjective experience of experts, is used to determine the coupled weights and optimize the risk coefficients, so as to construct a comprehensive cloud model for normal landing and hard landing segments. Numerical analysis is performed using flight data from 20 000 segments to demonstrate that the proposed method accurately maps QAR data in the landing phase to the risk evaluation space, and that the proposed method that incorporates risk weights improves the identification performance of hard landing segments significantly.

Key words: hard landing, risk evaluation, quick access recorder (QAR) data, coupled interaction, decision-making trial and evaluation laboratory (DEMATEL) method

CLC Number:

V328.1

Lei DONG, Yuanshan ZHANG, Xi CHEN, Jiachen LIU, Xinqi PENG. Comprehensive evaluation method for aircraft hard landing risk considering multiple coupled interactions[J]. Systems Engineering and Electronics, 2025, 47(4): 1265-1274.

Figures/Tables 18

Fig.1

Table 1

Fig.2

Fig.3

Fig.4

Table 2

Fig.5

Table 3

Table 4

Fig.6

Table 5

Fig.7

Table 6

Table 7

Table 8

Fig.8

Fig.9

Table 9

References 14

15	王鹏, 刘嘉琛, 董磊, 等. 面向任务的民机DIMA动态重构策略[J]. 系统工程与电子技术, 2021, 43 (6): 1618- 1627. doi: 10.12305/j.issn.1001-506X.2021.06.19
	WANG P , LIU J C , DONG L , et al. Task oriented DIMA dynamic reconfiguration strategy for civil aircraft[J]. Systems Engineering and Electronics, 2021, 43 (6): 1618- 1627. doi: 10.12305/j.issn.1001-506X.2021.06.19
16	BRYAN M. Flight data for tail 676/687[EB/OL]. [2023-10-15]. https://c3.ndc.nasa.gov/dashlink/projects/85/resources/.
17	BARDOU N, OWENS D. Hard landing, a case study for crews and maintenance personnel[EB/OL]. [2023-10-15]. https://safetyfirst.airbus.com/hard-landing-a-case-study-for-crews-and-maintenance-personnel/.
18	HIETE M , MERZ M , COMES T , et al. Trapezoidal fuzzy DEMATEL method to analyze and correct for relations between variables in a composite indicator for disaster resilience[J]. OR Spectrum, 2012, 34, 971- 995. doi: 10.1007/s00291-011-0269-9
19	DONG L , WANG P , YAN F . Damage forecasting based on multi-factor fuzzy time series and cloud model[J]. Journal of Intelligent Manufacturing, 2019, 30, 521- 538. doi: 10.1007/s10845-016-1264-4
20	郑磊, 池宏, 许保光, 等. 飞机重着陆预警分析方法[J]. 数学的实践与认识, 2019, 49 (3): 56- 72.
	ZHENG L , CHI H , XU B G , et al. Method of early analysis for aircraft hard landing[J]. Mathematics in Practice and Theory, 2019, 49 (3): 56- 72.
21	TONG C , YIN X , LI J , et al. An innovative deep architecture for aircraft hard landing prediction based on time-series sensor data[J]. Applied Soft Computing Journal, 2018, 73, 344- 349.
22	李旭. 基于飞行数据的民航客机重着陆可解释性研究[D]. 重庆: 重庆大学, 2021.
	LI X. Research on interpretability for hard landing incident of civil aircraft based on flight data[D]. Chongqing: Chongqing University, 2021.
23	QIAO X D, ZHANG W B, ZOU S H, et al. A prediction model of hard landing based on RBF neural network with K-means clustering algorithm[C]//Proc. of the IEEE International Conference on Industrial Engineering and Engineering Management, 2016: 462- 465.
24	CHEN H, ZHOU S H, XIE Y, et al. The study on hard landing prediction model with optimized parameter SVM method[C]// Proc. of the 35th Chinese Control Conference, 2016: 4283-4287.
25	SUN R S, LI C F. A risk prediction model of hard landing based on random forest algorithm[C]//Proc. of the E3S Web of Conferences, 2021.
1	The Boeing Company. Statistical summary of commercial jet airplane accidents worldwide operations 1959-2021[EB/OL]. [2023-11-15]. https://www.boeing.com/resources/boeingdotcom/company/about_bca/pdf/statsum.pdf.
2	MH/T2001-2018. 新版民用航空器事故征候[S]. 北京: 中国民用航空局, 2018.
	MH/T2001-2018. New version of civil aircraft accident symptoms[S]. Beijing: Civil Aviation Administration of China, 2018.
3	Doc 9859. Safety management manual[S]. Montreal: International Civil Aviation Organization, 2018.
4	European Union Aviation Safety Agency. Developing standardized FDM-based indicators[EB/OL]. [2023-11-15]. https://www.easa.europa.eu/en/downloads/13049/en.
5	AC 91-79A. Mitigating the risks of a runway overrun upon land ing[S]. Washington, DC: Federal Aviation Administration, 2016.
6	鲁志东, 张曙光, 戴闰志, 等. 大型民机进近着陆段异常能量风险判据[J]. 航空学报, 2021, 42 (6): 102- 115.
	LU Z D , ZHANG S G , DAI R Z , et al. Abnormal energy risk criteria of large civil airplanes in approach and landing[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42 (6): 102- 115.
7	陈农田, 满永政, 李俊辉. 基于QAR数据的民机高高原进近着陆风险评估方法[J]. 北京航空航天大学学报, 2024, 50 (1): 77- 85.
	CHEN N T , MAN Y Z , LI J H . Risk assessment method of civ il aircraft approach and landing at high plateau based on QAR data[J]. Journal of Beijing University of Aeronautics and Astronautics, 2024, 50 (1): 77- 85.
8	LU X, CHEN D W, ZHANG F T. Grey correlation analysis of hard landing based on QAR data[C]//Proc. of the IEEE International Conference on Civil Aviation Safety and Information Technology, 2019: 79-83.
9	WANG L, WU C X, SUN R S, et al. An analysis of hard landing incidents based on flight QAR data[C]//Proc. of the 11th International Conference on Engineering Psychology and Cognitive Ergonomics, 2014: 398-406.
10	王冉, 高振兴. 基于飞行数据的民机着陆安全影响因素研究[J]. 交通信息与安全, 2019, 37 (4): 27- 34. doi: 10.3963/j.issn.1674-4861.2019.04.004
	WANG R , GAO Z X . Influencing factors of civil aircraft landing safety based on flight data[J]. Journal of Transport Information and Safety, 2019, 37 (4): 27- 34. doi: 10.3963/j.issn.1674-4861.2019.04.004
11	史佳辉, 徐吉辉, 陈玉金, 等. 基于交互作用矩阵多维云模型的飞机重着陆风险评估方法研究[J]. 系统工程与电子技术, 2021, 43 (10): 3026- 3032. doi: 10.12305/j.issn.1001-506X.2021.10.39
	SHI J H , XU J H , CHEN Y J , et al. Research on risk assessment method of aircraft heavy landing based on interaction matrix-multidimensional cloud model[J]. Systems Engineering and Electronics, 2021, 43 (10): 3026- 3032. doi: 10.12305/j.issn.1001-506X.2021.10.39
12	SI S L , YOU X Y , LIU H C , et al. DEMATEL technique: a systematic review of the state-of-the-art literature on methodo-logies and applications[J]. Mathematical Problems in Engineering, 2018, 20183696457.
13	CHEN H , LIU S , WANYAN X R , et al. Influencing factors of novice pilot SA based on DEMATEL-AISM method: from pilots' view[J]. Heliyon, 2023, 9 (2): e13425. doi: 10.1016/j.heliyon.2023.e13425
14	JIAO J , WEI M W , YUAN Y , et al. Risk quantification and analysis of coupled factors based on the DEMATEL model and a Bayesian network[J]. Applied Sciences, 2019, 10 (1): 317. doi: 10.3390/app10010317

类型	参数	描述	单位	采样频率/Hz
动力学变量	AIL_1/2	左/右侧副翼位置	(°)	1
	ALT	气压高度	ft	4
	ALTR	升降率	ft/min	4
	AOA_1/2	攻角1/2	(°)	4
	AOAC	矫正攻角	(°)	4
	AOAI	指示攻角	(°)	4
	BAL_1/2	气压矫正高度1/2	ft	4
	BLAC	机身纵向加速度	g	16
	CAS	计算空速	Kt	4
	CTAC	横向加速度	g	16
	DA	偏航角	(°)	4
	ELEV_1/2	左/右侧升降舵位置	(°)	1
	FPAC	飞行轨迹加速度	g	16
	VRTG	垂直加速度	g	8
飞行员操作	CCPC	机长操纵杆位置	COUNTS	2
	CCPF	副驾驶操纵杆位置	COUNTS	2
	CWPF	副驾驶控制轮位置	COUNTS	2
大气环境因素	WD	真风向	(°)	4
大气环境因素	WS	风速	Kt	4
指示参数	DWPT	到路径点的距离	—	1
	WOW	轮胎上(有无)重量	—	1
	EAI	发动机除冰系统(开关)	—	1
⋮	⋮	⋮	⋮	⋮

特征	排序
下降率ALTR	1
垂直加速度VRTG	2
副翼位置2 AIL_2	3
攻角1 AOA_1	4
气压矫正高度2 BAL_2	5
计算空速CAS	6
机身纵向加速度BLAC	7
⋮	⋮

等级	接地ALTR偏差/(ft/min)	接地CAS偏差/(km/h)	BAL偏差/m
1	-493.2~-349.2	-18.71~-12.96	3.86~8.94
2	-349.2~0	-12.96~0	0~3.86
3	0~210.8	0~8.05	-8.94~0
4	210.8~349.2	8.05~10.76	-9.6~-8.94
5	349.2~598	10.76~12.96	-13.4~-9.6

语言变量	梯形模糊数
影响极小	(0.00, 0.00, 0.10, 0.20)
影响较小	(0.10, 0.20, 0.40, 0.50)
影响适中	(0.40, 0.50, 0.60, 0.70)
影响较大	(0.60, 0.70, 0.80, 0.90)
影响极大	(0.80, 0.90, 1.00, 1.00)

参数	飞行参数偏差计算标准值	飞行参数偏差值	风险系数
f₁	96.000 ft/min	-201.600 ft/min	0.350
f₂	194.24 Km/h	4.58 Km/h	0.736
f₃	247.95 m	2.96 m	0.268
f₄	1.150g	-0.061g	0.269
f₅	3.587°	1.142°	0.279
f₆	83.501°	4.241°	0.230

Comprehensive evaluation method for aircraft hard landing risk considering multiple coupled interactions

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 18

References 14

Related Articles 4

Recommended Articles

Metrics

Comments

参数	影响度	被影响度	原因度	中心度	多元耦合权重
f₁	0.985	1.609	-0.624	2.594	1.176 88
f₂	0.981	1.164	-0.183	2.145	0.390 54
f₃	0.895	1.564	-0.669	2.459	0.940 46
f₄	1.009	1.518	-0.509	2.527	1.059 54
f₅	1.634	0.695	0.939	2.329	0.712 79
f₆	1.881	0.835	1.046	2.716	1.390 54

参数	偏差值	风险系数	多元耦合权重	优化后风险系数
f₁	-201.600 ft/min	0.350	1.176 88	0.411
f₂	4.58 km/h	0.736	0.390 54	0.287
f₃	2.96 m	0.268	0.940 46	0.252
f₄	-0.061g	0.269	1.059 54	0.285
f₅	1.142°	0.279	0.712 79	0.199
f₆	4.241°	0.230	1.390 54	0.320

方法	Precision	Recall	Accuracy
文献[20]	96	67	68
文献[21]	53	49	53
Block-LSTM^[22]	95	80	88
文献[23]	72	68	69
文献[24]	84	53	71
文献[25]	87	85	87
Hu等+半监督聚类^[22]	62	61	62
本文所提方法(风险系数未优化)	95	69	69
本文所提方法(风险系数优化)	99	82	81

[1]	Han ZHANG, Qiang WANG. Aviation safety accident risk identification and evaluation based on directed networks [J]. Systems Engineering and Electronics, 2024, 46(6): 1995-2001.
[2]	SHI Zhijian, WANG Huawei, WANG Xiang. Risk state evaluation of aviation maintenance based on multiple connection number set pair analysis [J]. Systems Engineering and Electronics, 2016, 38(3): 588-594.
[3]	LI Ting-peng, LI Yue, XU Yong-cheng, QIAN Yan-ling. Modeling and analysis of operation support progress of equipment based on improved PERT [J]. Systems Engineering and Electronics, 2015, 37(7): 1575-1580.
[4]	LIU Dong-liang, XU Hao-jun, MIN Gui-long. Stability analysis of aeronautic man-machine complex system risk evaluation by extremum theory [J]. Journal of Systems Engineering and Electronics, 2012, 34(12): 2504-2508.

参数	取值	参数	取值
Ex	0.293	s²	0.005
En	0.061	He	0.036