Quantitative evaluation of pilots' unsafe operation behavior driven by QAR data

doi:10.16265/j.cnki.issn1003-3033.2023.05.2300

Abstract

Abstract:

To achieve a quantitative evaluation of pilots' unsafe operation behavior, this study proposed a method based on QAR data to evaluate pilots' unsafe operation behavior. Firstly, the theory and classification methods of human unsafe behavior were analyzed, and then five evaluation indexes (Time deviation, Sequence error, Omission, Redundancy, Over limit) were proposed from the time dimension and space dimension based on pilots' unsafe operation behavior characteristics. Then, 78 flight experts were asked to classify the unsafe events of FOQA according to the five evaluation indexes mentioned above. After that, the quantitative evaluation index system of unsafe operation behavior of pilots was formed, which covered the whole flight operation process. Finally, a quantitative evaluation model of pilots' unsafe operation behavior was constructed, and the quantitative calculation of pilots' unsafe operation behavior was realized by combining QAR data. The results show that this method can be used for quantitative evaluation of pilots' unsafe operation behaviors such as error and violation, and provide a reference for airlines to carry out specific risk management and training of unsafe behaviors.

Key words: quick access recorder (QAR) data, pilots, unsafe operation behavior, quantitative evaluation, flight operations quality assurance (FOQA)

WANG Lei, ZOU Ying, WANG Shuo. Quantitative evaluation of pilots' unsafe operation behavior driven by QAR data[J]. China Safety Science Journal, 2023, 33(5): 49-56.

Figures/Tables 5

Tab.1

Tab.2

Tab.3

Tab.4

Secondary evaluation indexes' score of pilot K's 12 flights

航班次数j	评价指标分量取值及得分
	m₁		m₂		m₃		m₄		m₅		m₆		m₇		m₈
	$q 1 j$ /ft	$t 1 j$	$q 2 j$ /ft	$t 2 j$	$q 3 j$	$s 1 j$	$q 4 j$	$o 1 j$	$q 5 j$	$o 2 j$	$q 6 j$	$r 1 j$	$q 7 j$ /kts	$l 1 j$	$q 8 j$ /g	$l 2 j$
1	2 140	0	2 140	0	否	0	否	0	否	0	否	0	27	10	1.46	10
2	2 230	0	2 060	0	否	0	否	0	否	0	否	0	23	10	1.43	0
3	3 200	0	2 760	0	否	0	否	0	否	0	否	0	24	10	1.43	0
4	3 140	0	2 850	0	否	0	否	0	否	0	否	0	25	10	1.42	0
5	2 190	0	2 160	0	否	0	否	0	否	0	否	0	26	10	1.29	0
6	3 170	0	2 450	0	否	0	否	0	否	0	否	0	21	0	1.23	0
7	2 220	0	2 140	0	否	0	否	0	否	0	否	0	26	10	1.45	0
8	3 280	0	2 290	0	否	0	否	0	否	0	否	0	27	10	1.32	0
9	2 160	0	2 140	0	否	0	否	0	否	0	否	0	27	10	1.36	0
10	2 040	0	2 040	0	否	0	否	0	否	0	否	0	20	0	1.34	0
11	2 240	0	2 090	0	否	0	否	0	否	0	否	0	12	0	1.29	0
12	2 690	0	2 420	0	否	0	否	0	否	0	否	0	18	0	1.38	0

Tab.4

Tab.5

References 29

[1]	中国民用航空局航空安全办公室. 中国民航航空安全报告(2021年)[R], 2022.6.
[2]	International Air Transport Association, Safety report 2021[R], 2022.4.
[3]	COOPER G E, HARPER R P. The use of pilot rating in the evaluation of aircraft handling qualities[M]. Washington: National Aeronautics and Space Administration, 1969: 7-39.
[4]	NORMAN D A. Categorization of action slips[J]. Psychological Review, 1981, 88(1): 1-15. doi: 10.1037/0033-295X.88.1.1
[5]	REASON J. Human error[M]. London: Cambridge University Press, 1990: 1-202.
[6]	RASMUSSEN J. Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1983, SMC-13(3): 257-266. doi: 10.1109/TSMC.1983.6313160
[7]	REASON J, MANSTEAD A, STRADLING S, et al. Errors and violations on the roads: a real distinction?[J]. Ergonomics, 1990, 33(10/11): 1315-1332. doi: 10.1080/00140139008925335
[8]	SHAPPELL S A, WIEGMANN D A. The human factors analysis and classification system:HFACS[R]. FAA, 2000.
[9]	WIEGMANN D A, SHAPPELL S A. A human error approach to aviation accident analysis: the human factors analysis and classification system[M]. London: Ashgate Publishing, 2003:1-98.
[10]	MORROW D, NORTH R, WICKENS C D. Reducing and mitigating human error in medicine[J]. Reviews of Human Factors and Ergonomics, 2005, 1(1): 254-296. doi: 10.1518/155723405783703019
[11]	PARASURAMAN R, SHERIDAN T B, WICKENS C D. A model for types and levels of human interaction with automation[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2000, 30(3): 286-297. doi: 10.1109/3468.844354
[12]	KELLY D, EFTHYMIOUS M. An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017[J]. Journal of Safety Research, 2019, 69: 155-165. doi: S0022-4375(18)30840-5 pmid: 31235226
[13]	江浩. 基于HFACS模型的航空事故人因分析及其安全管理体系建设研究[D]. 西安: 陕西师范大学, 2015.
	JIANG Hao. Research on human factors analysis and safety management system construction of aviation accidents based on HFACS model[D]. Xi'an: Shaanxi Normal University, 2015.
[14]	SHORROCK S T, KIRWAN B. Development and application of a human error identification tool for air traffic control[J]. Applied Ergonomics, 2002, 33(4): 319-336. pmid: 12160336
[15]	ISAAC A, SHORROCK S T, KIRWAN B. Human error in European air traffic management: the HERA project[J]. Reliability Engineering & System Safety, 2002, 75(2): 257-272. doi: 10.1016/S0951-8320(01)00099-0
[16]	杨越, 宋祥波, 王建忠. 基于TRACEr的管制员差错风险量化分析[J]. 中国安全科学学报, 2020, 30(8):109-115. doi: 10.16265/j.cnki.issn1003-3033.2020.08.016
	YANG Yue, SONG Xiangboe, WANG Jianzhong. Quantitative analysis method of ATCO's error risks based on TRACEr[J]. China Safety Science Journal, 2020, 30(8):109-115. doi: 10.16265/j.cnki.issn1003-3033.2020.08.016
[17]	HOLLNAGEL E. Cognitive reliability and error analysis method (CREAM)[M]. Oxford: Elsevier, 1998:15-19.
[18]	PAYNE K H, HARRIS D. A psychometric approach to the development of a multidimensional scale to assess aircraft handling qualities[J]. The International Journal of Aviation Psychology, 2000, 10(4): 343-362. doi: 10.1207/S15327108IJAP1004_3
[19]	游旭群, 姬鸣, 戴鲲, 等. 航线驾驶安全行为多维评价量表的构建[J]. 心理学报, 2009, 41(12):1237-1251.
	YOU Xuqun, JI Ming, DAI Kun, et al. Developing a multidimensional scale to assess safety behaviors in airline flight[J]. Acta Psychologica Sinica, 2009, 41(12):1237-1251.
[20]	段照斌, 杜海龙, 张鹏. 基于QAR2Vec模型的QAR数据特征提取[J]. 中国安全科学学报, 2021, 31(1):145-152. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.021
	DUAN Zhaobin, DU Hailong, ZHANG Peng. Feature extraction of QAR data based on QAR2Vec model[J]. China Safety Science Journal, 2021, 31(1):145-152. doi: 10.16265/j.cnki.issn 1003-3033.2021.01.021
[21]	孙瑞山, 肖亚兵. 基于QAR记录数据的民航飞行员操作特征指标结构研究[J]. 中国安全生产科学技术, 2012, 8(11):49-54.
	SUN Ruishan, XIAO Yabing. Research on indicating structure for operation characteristic of civil aviation pilots based on QAR data[J]. Journal of Safety Science and Technology, 2012, 8(11):49-54.
[22]	郑磊, 池宏, 邵雪焱. 基于QAR数据的飞行操作模式及其风险分析[J]. 中国管理科学, 2017, 25(10):109-118.
	ZHENG Lei, CHI Hong, SHAO Xueyan. Pattern recognition and risk analysis for flight operations[J]. Chinese Journal of Management Science, 2017, 25(10):109-118.
[23]	汪磊, 董传亭, 杨星月, 等. 民航飞行员风险心理与操作水平相关性研究[J]. 中国安全科学学报, 2019, 29(6):37-42. doi: 10.16265/j.cnki.issn1003-3033.2019.06.007
	WANG Lei, DONG Chuanting, YANG Xingyue, et al. Correlation study of airline pilots' psychology of risk and flight operation[J]. China Safety Science Journal, 2019, 29(6):37-42. doi: 10.16265/j.cnki.issn1003-3033.2019.06.007
[24]	SWAIN A D. A method for performing a human factors reliability analysis[M]. Alberquerque: Sandia Corporation, 1963:7-8.
[25]	ALTMAN J W. Improvements needed in a central store of human performance data[J]. Human Factors, 1964, 6: 681-686. pmid: 14335482
[26]	李政仪, 花迎春, 孙志强, 等. 基于差错分类框架辨识人为差错[J]. 中国安全科学学报, 2012, 22(2):94-99.
	LI Zhengyi, HUA Yingchun, SUN Zhiqiang, et al. Human error identification guided by error classification frameworks[J]. China Safety Science Journal, 2012, 22(2):94-99.
[27]	水祎舟, 游旭群, 万炳军. 基于GAMLSS模型的我国青少年体质健康评价指标百分位数分布曲线及参考值[J]. 武汉体育学院学报, 2021, 55(11):77-84,100.
	SHUI Yizhou, YOU Xuqun, WAN Bingjun. Percentile distribution curve and reference value of physical health evaluation index of chinese adolescents based on GAMLSS model[J]. Journal of Wuhan Institute of Physical Education, 2021, 55(11):77-84,100.
[28]	赵新斌, 李斌. 异常值检测方法在民航告警中的应用[J]. 南京航空航天大学学报, 2017, 49(4):524-530.
	ZHAO Xinbin, LI Bin. Application of outlier detection method in civil aviation alarm[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2017, 49(4):524-530.
[29]	汪磊, 高杉, 张静怡, 等. 民航飞行员超限行为评价方法研究[J]. 安全与环境学报, 2021, 21(2):695-700.
	WANG Lei, GAO Shan, ZHANG Jingyi, et al. Evaluation of the exceedance behaviors of the airline transport pilots based on the QAR data[J]. Journal of Safety and Environment, 2021, 21(2):695-700.

评价维度	评价指标	评价指标定义	评价指标数学表达式
时间维度	偏时性	飞行员在飞行操作过程中,未按照规定要求,及时开始/完成某项飞行操纵环节,操作过早或过晚	1:00→0:50/1:10
时间维度	错序性	飞行员在飞行操作过程中,未按照规定的操作先后顺序进行操纵,操作顺序有误	1→3→2→4
空间维度	遗漏性	飞行员在飞行操作过程中,未按照规定要求,遗漏了必要飞行操作	1→0
	多余性	飞行员在飞行操作过程中,未按照规定要求,执行多余或错误的飞行操作	0→1
	超限性	飞行员在操作过程中,未将飞机的速度、姿态控制在限制范围内	(100,200)→ 100^-/200⁺

评价指标	飞行阶段	评价指标分量	监控参数
偏时性	进近	进近放起落架晚	起落架-高度
偏时性	进近	着陆襟翼到位晚	襟翼-高度
错序性	进近	进近襟翼错序	襟翼
错序性	复飞	复飞襟翼错序	襟翼
遗漏性	进近	进近阶段减速板未预位	减速板
遗漏性	着陆	着陆不使用反推	反推
多余性	巡航	巡航中自动驾驶仪脱开	自动驾驶
超限性	滑行	直线滑行速度大	地速
超限性	着陆	着陆垂直载荷大	载荷

评价指标	评价指标分量	无超限倾向 (0分)	倾向超限 (10分)	轻度超限 (20分)	严重超限 (30分)
偏时性	进近放起落架晚/ft	(1 991.6,+∞)	(1 500,1 991.6]	(1 300,1 500]	(0,1 200]
偏时性	着陆襟翼到位晚/ft	(1 852.6,+∞)	(1 200,1 852.6]	(1 000,1 200]	(0,1 000]
错序性	进近襟翼错序	未探测到	—	—	探测到
错序性	复飞襟翼错序	未探测到	—	—	探测到
遗漏性	进近阶段减速板未预位	未探测到	—	—	探测到
遗漏性	着陆不使用反推	未探测到	—	—	探测到
多余性	巡航中自动驾驶仪脱开	未探测到	—	—	探测到
超限性	直线滑行速度大/kts	[0,23)	[23,30)	[30,40)	[40,+∞)
超限性	着陆垂直载荷大/g	[0.80,1.46)	[1.46,1.68)	[1.68,1.89)	[1.89,+∞)

被试	航班量	偏时性	超限性	总分
1	12	0	3.75	3.75
2	14	4.29	3.21	7.50
3	17	1.18	1.76	2.94
︙	︙	︙	︙	︙
26	13	2.31	1.54	3.85
27	6	5.00	2.50	7.50
28	13	1.15	2.69	3.85