Flight training evaluation based on dynamic Bayesian network and fuzzy gray theory

LIU Hao; WANG Hao; MENG Guanglei; WU Hao; ZHOU Mingzhe

doi:10.7527/S1000-6893.2021.25838

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 8: 525838 - 525838

DOI: https://doi.org/10.7527/S1000-6893.2021.25838

Article

Flight training evaluation based on dynamic Bayesian network and fuzzy gray theory

LIU Hao ,
WANG Hao ,
MENG Guanglei ,
WU Hao ,
ZHOU Mingzhe

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1. AVIC Shenyang Aircraft Design and Research Institute, Shenyang 110035, China;
2. School of Automation, Shenyang Aerospace University, Shenyang 110136, China

Received date: 2021-04-15

Revised date: 2021-05-08

Online published: 2021-06-01

Supported by

Aeronautical Science Foundation of China (2016ZD54015)

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Abstract

An evaluation method for flight training of warplanes is proposed based on dynamic Bayesian network and fuzzy gray theory. Firstly, the causal relationship between typical flight parameters and maneuver in the training process is analyzed. The maneuver recognition model based on dynamic Bayesian network is constructed according to expert experience and prior knowledge, and the maneuver recognition results are obtained by reasoning. Then, an evaluation index system of fighter flight training is established. The evaluation index of flight training is selected according to the results of fighter maneuver identification, and the index weight is determined by the comprehensive weighting method. Finally, the gray fuzzy evaluation matrix is established, and the evaluation results are obtained by calculating the flight data of each evaluation index in the flight training process. The experimental results show that the evaluation method proposed can recognize maneuver and evaluate flight training according to the parameters in the flight process, improving the efficiency of flight training evaluation.

Key words： dynamic Bayesian network; maneuver recognition; gray fuzzy evaluation matrix; flight training evaluation; evaluation index system

Cite this article

LIU Hao , WANG Hao , MENG Guanglei , WU Hao , ZHOU Mingzhe . Flight training evaluation based on dynamic Bayesian network and fuzzy gray theory[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(8) : 525838 -525838 . DOI: 10.7527/S1000-6893.2021.25838

References

[1] 张建业, 李学仁, 倪世宏. 飞行成绩评定及管理系统[J]. 空军工程大学学报(自然科学版), 2001, 2(01):70-73. ZHANG J Y, LI X R, NI S H. Flight performance evaluation and management system[J]. Journal of Air Force Engineering University (Natural Science Edition), 2001, 2(1):70-73(in Chinese).
[2] 张福转, 孔庆波. 层次分析法在飞行模拟训练评价体系设计中的应用[J]. 电脑知识与技术, 2016, 12(11):270-271. ZHANG F Z, KONG Q B. Application of analytic hierarchy process in the design of flight simulation training evaluation system[J]. Computer Knowledge and Technology, 2016, 12(11):270-271(in Chinese).
[3] 高文琦, 张复春, 王立波, 等. 飞行训练成绩评估模型的建立与实现[J]. 电子设计工程, 2011, 19(24):50-52. GAO W Q, ZHANG F C, WANG L B, et al. Establishment and implementation of flight training performance evaluation model[J]. Electronic Design Engineering, 2011, 19(24):50-52(in Chinese).
[4] ABDUSSAMIE N, DABOOS M, ELFERJANI I, et al. Risk assessment of LNG and FLNG vessels during manoeuvring in open sea[J]. Journal of Ocean Engineering and Science, 2018, 3(1):56-66.
[5] 黄江中, 陈秀清, 许威, 等. 基于模糊灰度变换的水下图像增强技术研究[J]. 应用科技, 2018, 45(3):1-6. HUANG J Z, CHEN X Q, XU W, et al. Research on underwater image enhancement technology based on fuzzy gray transformation[J]. Applied Science and Technology, 2018, 45(3):1-6(in Chinese).
[6] 乔晋崴, 肖光春, 衣明东, 等. 基于模糊灰度理论的重卡消声器冲压生产线可靠性分配[J]. 齐鲁工业大学学报, 2019, 33(5):52-58. QIAO J X, XIAO G C, YI M D, et al. Reliability allocation of heavy truck muffler stamping production line based on fuzzy gray theory[J]. Journal of Qilu University of Technology, 2019, 33(5):52-58(in Chinese).
[7] 朱玉琴, 王旭, 叶琴, 等. 潮流发电装置综合评价方法[J]. 现代电力系统与清洁能源杂志, 2015, 4(4):702-708. ZHU Y Q, WANG X, YE Q, et al. Comprehensive evaluation method for tidal current power generation device[J]. Journal of Modern Power Systems & Clean Energy, 2015, 4(4):702-708(in Chinese).
[8] HAN W, YANG Z, HUANG L, et al. Fuzzy comprehensive evaluation of the effects of relative air humidity on the morpho-physiological traits of Pakchoi (Brassica chinensis L.) under high temperature[J]. Scientia Horticulturae, 2019, 246:971-978.
[9] LEE E W, WONG L T, MUI K W. Development of a hybrid artificial neural network model and its application to data regression[J]. Intelligent Automation & Soft Computing, 2012, 18(4):319-332.
[10] SREEKANTH T G, SENTHILKUMAR M, REDDY S M. Fatigue life evaluation of delaminated GFRP laminates using artificial neural networks[J]. Transactions of the Indian Institute of Metals, 2021(in press).
[11] 柳忠起, 袁修干, 樊瑜波. 基于BP神经网络的飞行绩效评价模型[J]. 北京航空航天大学学报, 2010, 36(4):403-406. LIU Z Q, YUAN X G, FAN Y B. Flight performance evaluation model based on BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(4):403-406(in Chinese).
[12] SU Z, YU S, CHU J, et al. A novel architecture:Using convolutional neural networks for Kansei attributes automatic evaluation and labeling[J]. Advanced Engineering Informatics, 2020, 44:101055.
[13] 孔祥芬, 王杰, 张兆民. 基于贝叶斯网络和共因失效的飞机电源系统可靠性分析[J]. 航空学报, 2020, 41(5):323632. KONG X F, WANG J, ZHANG Z M. Reliability analysis of aircraft power system based on Bayesian network and common cause failure[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5):323632(in Chinese).
[14] 于劲松, 沈琳, 唐荻音, 等. 基于贝叶斯网络的故障诊断系统性能评价[J]. 北京航空航天大学学报, 2016, 42(1):35-40. YU J S, SHEN L, TANG D Y, et al. Performance evaluation of fault diagnosis system based on Bayesian network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(01):35-40(in Chinese).
[15] 汪泽辉, 方洋旺. 基于贝叶斯网络的空战效能评估方法研究[J]. 航空工程进展, 2018, 9(1):35-42. WANG Z H, FANG Y W. Effectiveness evaluation method of air-combat based on Bayesian network[J]. Advances in Aeronautical Science and Engineering, 2018, 9(1):35-42(in Chinese).
[16] 孟光磊, 张慧敏, 朴海音, 等. 自动化飞行训练评估中的战机机动动作识别[J]. 北京航空航天大学学报, 2020, 46(7):1267-1274. MENG G L, ZHANG H M, PIAO H Y, et al. Maneuver recognition in automatic flight training evaluation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7):1267-1274(in Chinese).
[17] 王玉伟, 高永. 基于综合赋权法的无人机飞行质量综合评价方法[J]. 兵工自动化, 2019, 38(5):1-4, 10. WANG Y W, GAO Y. Comprehensive evaluation method of UAV flight quality based on comprehensive weighting method[J]. Ordnance Automation, 2019, 38(5):1-4, 10(in Chinese).
[18] DU M, GAO H, LI L, et al. Power grid security risk assessment based on the comprehensive element influence index[J]. IOP Conference Series:Earth and Environmental Science, 2019, 227(3):10.
[19] ZHAO Y, PAN L, XIN X, et al. Multiobjective evaluation of midblock crosswalks on urban streets based on TOPSIS and entropy methods[J]. Transportation Research Record Journal of the Transportation Research Board, 2016, 2586:59-71.
[20] WANG M, NIU D. Research on project post-evaluation of wind power based on improved ANP and fuzzy comprehensive evaluation model of trapezoid subordinate function improved by interval number[J]. Renewable Energy, 2019, 132(10):255-265.
[21] HOU B, HUANG Z, ZHOU H, et al. A hybrid hint-based and fuzzy comprehensive evaluation method for optimal parting curve generation in injection mold design[J]. The International Journal of Advanced Manufacturing Technology, 2021, 112(12):2133-2148.
[22] WEI J, LI G, XIE D, et al. Discrimination of mine water-inflow sources based on the multivariate mixed model and fuzzy comprehensive evaluation[J]. Arabian Journal of Geosciences, 2020, 13(17):873.
[23] YANG Y F, ZHANG M H, DAI Y W, et al. A fuzzy comprehensive CS-SVR model-based health status evaluation of radar[J]. PLoS One, 2019, 14(3):e0213833.
[24] SUN Y, WANG L, XU J, et al. An intelligent coupling 3-grade fuzzy comprehensive evaluation approach with AHP for selection of levitation controller of maglev trains[J]. IEEE Access, 2020, 8:99509-99518.
[25] 卜广志, 张宇文. 基于灰色模糊关系的灰色模糊综合评判[J]. 系统工程理论与实践, 2002, 22(4):141-144. BU G Z, ZHANG Y W. Grey fuzzy comprehensive evaluation based on the the theory of grey fuzzy relation[J]. Systems Engineering-Theory & Practice, 2002, 22(4):141-144. (in Chinese)
[26] 国连玉, 李可军, 梁永亮, 等. 基于灰色模糊综合评判的高压断路器状态评估[J]. 电力自动化设备, 2014, 34(11):161-167. GUO L Y, LI K J, LIANG Y L, et al. HV circuit breaker state assessment based on gray-fuzzy comprehensive evaluation[J]. Electric Power Automation Equipment, 2014, 34(11):161-167(in Chinese).
[27] SU L, LI H, LI Z, et al. Identification of Unbalanced bids based on grey-fuzzy evaluation method[J]. Canadian Journal of Civil Engineering, 2019, 47(3):272-278.
[28] YASUDA M, SEKIMOTO K. Spatial Monte Carlo integration with annealed importance sampling[J]. Physical Review E, 2021, 103(5):052118.
[29] SIFERD R, MAYBECK P. Monte Carlo approach for fatigue and damage calculation of nonlinear dynamical systems[J]. Chaos, 2020, 30(7):073135.
[30] BAREL A V, VANDEWALLE S. Robust optimization with a multilevel Monte Carlo method[J]. SIAM/ASA Journal on Uncertainty Quantification, 2019, 7(1):174-202.

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