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

doi:10.12305/j.issn.1001-506X.2021.10.39

摘要/Abstract

摘要：

针对飞机重着陆风险具有的系统性、复杂性、多因素影响等特征, 从系统角度入手, 提出一种能反映因素-因素、因素-系统间交互作用的模型—交互作用矩阵。采取多维云模型对其编码进行改进, 可有效弱化交互作用矩阵的主观性; 选取接地速度偏差、接地仰角偏差、接地垂直加速度和接地距离偏差作为重着陆风险的影响因素, 以系统的角度确定因素重要度; 结合每个风险因素以多维云理论生成5朵四维风险等级云模型, 以软件程序实现实例例证, 与实际风险等级、组合赋权云、证据-云评估结果保持一致, 证明所提方法的合理有效, 为降低着陆阶段风险提供参考。

关键词: 重着陆, 交互作用矩阵, 多维云, 风险评估

Abstract:

Aiming at the systemicity, complexity and multi-factor influence of the aircraft's heavy landing risk, this paper starts from the system perspective and proposes a model that can reflect the interaction between factors-factors and factors-systems—interaction matrix. Using multi-dimensional clouds to improve its coding, it can effectively weaken the subjectivity of the interaction matrix. Then, ground speed deviation, ground elevation deviation, ground vertical acceleration and ground distance deviation are selected as the influencing factors of the heavy landing risk determining the importance of the factors based on the system angle. Then, this paper combines each risk factor with multi-dimensional cloud theory to generate five four-dimensional risk grade cloud models and realizes examples with software programs. It is consistent with the actual risk level, combined weighting cloud and evidence-cloud assessment results which proves that the proposed method is reasonable and effective and provides a reference for reducing risks in the landing stage.

Key words: heavy landing, interaction matrix, multidimensional cloud, risk assessment

中图分类号:

X949

史佳辉, 徐吉辉, 陈玉金, 王晓琳. 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.

Jiahui SHI, Jihui XU, Yujin CHEN, Xiaolin WANG. 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.

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参考文献 30

1	MH/T2001-2018中国民用航空局. 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 Civil Aviation Administration of China. New version of civil aircraft accident symptoms[S]. Beijing: Civil Aviation Administration of China, 2018.
2	WU S H , LIU X D , LI Z X , et al. A consistency improving method in the analytic hierarchy process based on directed circuit analysis[J]. Journal of Systems Engineering and Electronics, 2019, 30 (6): 1160- 1181. doi: 10.21629/JSEE.2019.06.11
3	REZA F , MOHAMMAD K . Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment[J]. Safety Science, 2018, 102, 290- 300. doi: 10.1016/j.ssci.2017.10.018
4	SIMON L , WEI Z . Risk analysis for the supplier selection problem using failure modes and effects analysis (FMEA)[J]. Journal of Intelligent Manufacturing, 2016, 27 (6): 1309- 1321. doi: 10.1007/s10845-014-0953-0
5	LIU H C , HU Y P , WANG J J . Failure mode and effects analysis using two-dimensional uncertain linguistic variables and alternative queuing method[J]. IEEE Trans.on Reliability, 2019, 68 (2): 554- 565. doi: 10.1109/TR.2018.2866029
6	LI H W , JAMES J H , LI OU . A novel multiple-criteria decision-making-based FMEA model for risk assessment[J]. Applied Soft Computing Journal, 2018, 73, 684- 696. doi: 10.1016/j.asoc.2018.09.020
7	TIAN Z P , WANG J Q , ZHANG H Y . An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods[J]. Applied Soft Computing, 2018, 72, 636- 646. doi: 10.1016/j.asoc.2018.03.037
8	DANESHVAR S , YAZDI M , ADESINA K A . Fuzzy smart failure modes and effects analysis to improve safety performance of system: case study of an aircraft landing system[J]. Quality and Reliability Engineering International, 2020, 36 (3): 890- 909. doi: 10.1002/qre.2607
9	ZHOU L , LIU B S , ZHAO Y , et al. Application research of grey fuzzy evaluation method in enterprise product reputation evaluation[J]. Procedia CIRP, 2019, 83, 759- 766. doi: 10.1016/j.procir.2019.05.014
10	TONG C , YIN X , LI J , et al. An innovative deep architecture for aircraft heavy landing prediction based on time-series sensor data[J]. Applied Soft Computing, 2018, 73, 344- 349. doi: 10.1016/j.asoc.2018.07.061
11	SARTOR P , BECKER W , WORDEN K , et al. Bayesian sensitivity analysis of flight parameters in a hard-landing analysis process[J]. Journal of Aircraft, 2016, 53 (5): 1317- 1331. doi: 10.2514/1.C032757
12	ZHOU S H , ZHOU Y L , XU Z Z , et al. The landing safety prediction model by integrating pattern recognition and Markov chain with flight data[J]. Neural Computing & Applications, 2019, 31, 147- 159.
13	DAI Y , TIAN J , RONG H , et al. Hybrid safety analysis method based on SVM and RST: An application to carrier landing of aircraft[J]. Safety Science, 2015, 80, 56- 65. doi: 10.1016/j.ssci.2015.07.006
14	汪磊, 郭世广, 任勇. 基于飞行数据正态云的着陆操作风险评价方法[J]. 安全与环境学报, 2019, 19 (5): 1555- 1561.
	WANG L , GUO S G , REN Y . Landing operation risk evaluation based on the normal cloud of the flight data[J]. Journal of Safety and Environment, 2019, 19 (5): 1555- 1561.
15	魏博文, 黄海鹏, 徐镇凯. 基于云模型和组合赋权的岩体质量二维评价模型[J]. 岩石力学与工程学报, 2016, 35 (S1): 3092- 3099.
	WEI B W , HUANG H P , XU Z K . Two-dimensional evaluation model of rock mass based on combination weighting and cloud model[J]. Chinese Journal of Rock Mechanics and Engineering, 2016, 35 (S1): 3092- 3099.
16	沈延安, 张君彪. 基于云模型和证据理论的装备管理绩效评价[J]. 系统工程与电子技术, 2019, 41 (5): 1049- 1055.
	SHEN Y A , ZHANG J B . Equipment management perfor-mance assessment based on cloud model and evidence theory[J]. Systems Engineering and Electronics, 2019, 41 (5): 1049- 1055.
17	汪磊, 孙瑞山, 吴昌旭, 等. 基于飞行QAR数据的重着陆风险定量评价模型[J]. 中国安全科学学报, 2014, (2): 88- 92.
	WANG L , SUN R S , WU C X . A flight QAR data based model for heavy landing risk quantitative evaluation[J]. China Safety Science Journal, 2014, 24 (2): 88- 92.
18	朱荟群. 民机着陆阶段驾驶舱人机交互风险评估方法研究[D]. 南京: 南京航空航天大学, 2019.
	ZHU H Q. Research on risk assessment method of human-machine interaction in civil aircraft cockpit during landing phase[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019.
19	JIAO H , HUDSON J A . The fully-coupled model for rock engineering systems[J]. International Journal of Rock Mechanics & Mining Sciences & Geomechanics Abstracts, 1995, 32 (5): 491- 512.
20	任勇. 航线飞行员的风险倾向特征研究[D]. 天津: 中国民航大学, 2018.
	REN Y. Study on the characteristics of risk propensity of airline pilots[D]. Tianjin: Civil Aviation University of China, 2018.
21	NAGHADEHI M Z , JIMENEZ R , KHALOKAKAIE R , et al. A new open-pit mine slope instability index defined using the improved rock engineering systems approach[J]. International Journal of Rock Mechanics & Mining Sciences, 2013, 61, 1- 14.
22	WANG D , ZENG D B , SINGH V P , et al. A multidimension cloud model-based approach for water quality assessment[J]. Environmental Research, 2016, 149, 113- 121. doi: 10.1016/j.envres.2016.05.012
23	过江, 张为星, 赵岩. 岩爆预测的多维云模型综合评判方法[J]. 岩石力学与工程学报, 2018, 37 (5): 1199- 1206.
	GUO J , ZHANG W X , ZHAO Y . A multidimensional cloud model for rock burst prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 2018, 37 (5): 1199- 1206.
24	JALALIFAR H , MOJEDIFAR S , SAHEBI A A , et al. Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system[J]. Computers & Geotechnics, 2011, 38 (6): 783- 790.
25	王双川, 贾希胜, 胡起伟, 等. 基于正态灰云模型的装备维修保障系统效能评估[J]. 系统工程与电子技术, 2019, 41 (7): 1576- 1582.
	WANG S C , JIA X S , HU Q W , et al. Effectiveness evaluation for equipment maintenance support system based on normal grey cloud model[J]. Systems Engineering and Electronics, 2019, 41 (7): 1576- 1582.
26	SUN X Y , ZHANG J Y , HAN G . Application of a grey cloud model in the identification of defects in bolt anchorage[J]. INSIGHT, 2019, 61 (8): 465- 471. doi: 10.1784/insi.2019.61.8.465
27	XUE Y G , LI Z Q , LI S C . Water inrush risk assessment for an undersea tunnel crossing a fault: an analytical model[J]. Marine Georesources & Geotechnology, 2019, 37 (7): 816- 827.
28	PENG H G , ZHANG H Y , WANG J Q . An uncertain Z-number multicriteria group decision-making method with cloud mo-dels[J]. Information Sciences, 2019, 501, 136- 154. doi: 10.1016/j.ins.2019.05.090
29	ARAUJO L N , BELOTTI J T , ALVES T A , et al. Ensemble method based on artificial neural networks to estimate air pollution health risks[J]. Environmental Modelling and Software, 2020, 123, 104- 567.
30	ADAMS M D , KANAROGLOU P S . Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models[J]. Journal of Environmental Management, 2016, 168, 133- 141.

等级描述	数字特征参数值
等级描述	Ex	En	He
无	1	0.437	0.073
弱	2	0.707	0.118
中等	3	0.437	0.073
强	4	0.707	0.118
决定性	5	0.437	0.073

等级	接地速度偏差/(m·s^-1)	接地仰角偏差/(°)	接地垂直加速度/g	接地距离偏差/m
Ⅰ	0~0.147	0~0.151	0~0.145	0~0.153
Ⅱ	0.147~0.442	0.151~0.468	0.145~0.435	0.153~0.458
Ⅲ	0.442~0.737	0.468~0.754	0.435~0.725	0.458~0.764
Ⅳ	0.737~0.884	0.754~0.905	0.725~0.870	0.764~0.917
Ⅴ	0.884~1	0.905~1	0.870~1	0.917~1

等级	数字特征	接地速度偏差/(m·s^-1)	接地仰角偏差/(°)	接地垂直加速度/g	接地距离偏差/m
Ⅰ	Ex	0.000 0	0.000 0	0.000 0	0.000 0
	En	0.049 0	0.005 1	0.048 3	0.051 0
	He	0.020 0	0.020 0	0.020 0	0.020 0
Ⅱ	Ex	0.294 5	0.309 5	0.290 0	0.305 5
	En	0.098 3	0.105 7	0.096 7	0.101 7
	He	0.020 0	0.020 0	0.020 0	0.020 0
Ⅲ	Ex	0.589 5	0.611 0	0.580 0	0.611 0
	En	0.098 3	0.095 3	0.096 7	0.102 0
	He	0.020 0	0.020 0	0.020 0	0.020 0
Ⅳ	Ex	0.810 5	0.829 5	0.797 5	0.840 5
	En	0.049 0	0.050 3	0.048 3	0.051 0
	He	0.020 0	0.020 0	0.020 0	0.020 0
Ⅴ	Ex	1.000 0	1.000 0	1.000 0	1.000 0
	En	0.038 7	0.031 7	0.043 3	0.027 7
	He	0.020 0	0.020 0	0.020 0	0.020 0

交互作用矩阵特征值	数字特征	接地速度偏差/(m·s^-1)	接地仰角偏差/(°)	接地垂直加速度/g	接地距离偏差/m
C	Ex	2.888 9	3.111 1	2.444 4	2.222 2
	En	0.574 8	0.546 2	0.537 8	0.561 1
	He	0.174 0	0.156 8	0.156 8	0.156 8
E	Ex	2.222 2	2.777 8	3.333 3	2.333 3
	En	0.561 1	0.544 7	0.596 7	0.498 8
	He	0.156 8	0.165 6	0.174 1	0.147 4
C+E	Ex	5.111 1	5.888 9	5.777 7	4.555 5
	En	0.803 2	0.771 3	0.803 3	0.750 7
	He	0.234 2	0.228 1	0.234 3	0.215 2
w_i/%		23.96	27.60	27.08	21.36

着陆段	等级隶属度					实际情况	组合赋权云^[15]	证据-云^[16]	本文方法
着陆段	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	实际情况	组合赋权云^[15]	证据-云^[16]	本文方法
A1	0.133 2	0.425 1	0.774 1	0.965 5	0.569 4	Ⅳ	Ⅳ	Ⅳ	Ⅳ
A2	0.436 5	0.885 9	0.948 7	0.884 1	0.300 2	Ⅲ	Ⅳ	Ⅲ	Ⅲ
A3	0.802 1	0.987 1	0.763 1	0.503 7	0.122 1	Ⅱ	Ⅱ	Ⅱ	Ⅱ
A4	0.923 8	0.912 5	0.600 3	0.359 9	0.076 5	I	I	I	I~Ⅱ