Prediction of pilot human errors during the carrier landing phase under complex task environments based on an improved CREAM method

doi:10.16265/j.cnki.issn1003-3033.2026.02.0102

Abstract

Abstract:

To improve the safety of aircraft landing phase, this study investigates the prediction of pilot human error during the carrier landing phase complex task environments based on CREAM. Based on the pilot operational workflows during the carrier landing phase, this work recalibrates the original Common Performance Condition (CPC) factors in CREAM, and proposes an improved approach for predicting the probability of human error in pilots based on the improved CREAM method, describing the situational environment in which the pilot is located during the carrier landing phase. It introduces an Environmental Impact Index and Effect Impact Index to characterize the influence of various task environments on pilots' cognitive functions. A simulated carrier landing assessment experiment is designed and conducted based on the actual situational environment during the carrier landing phase. By analyzing characteristic indicators such as electroencephalogram(EEG), eye-tracking, electrocardiogram(ECG), and electromyography(EMG)data, and National Aeronautics and Space Administration Task Load Index(NASA-TLX)scale data of the subjects, the load level of the subjects in different environments is assessed, and then the effect impact index of different cognitive functions is calculated to predict the probability of human error. The results demonstrate that the proposed method for predicting probability of human error in pilots can improve the accuracy of prediction results by using objective data assessment, which instead of experts' subjective evaluations. This method can predict the probability of human error in pilots under the influence of 20 different environmental factors.

Key words: cognitive reliability and error analysis method(CREAM), carrier landing phase, pilot, complex task environments, human error, cognitive function

CLC Number:

X949

LIU Weicheng, SUN Youchao, JIN Heng, CHEN Zichang. Prediction of pilot human errors during the carrier landing phase under complex task environments based on an improved CREAM method[J]. China Safety Science Journal, 2026, 36(2): 44-51.

Figures/Tables 8

Table 1

Table 2

Table 3

Table 4

Table 5

Fig.1

Fig.2

Table 6

References 27

[1]	张志冰, 甄子洋, 江驹, 等. 舰载机自动着舰引导与控制综述[J]. 南京航空航天大学学报, 2018, 50(6):734-744.
	ZHANG Zhibing, ZHEN Ziyang, JIANG Ju, et al. Review on development in guidance and control of automatic carrier landing of carrier-based aircraft[J]. Journal of Nanjing University of Aeronautics& Astronautics, 2018, 50(6): 734-744.
[2]	甄子洋, 王新华, 江驹, 等. 舰载机自动着舰引导与控制研究进展[J]. 航空学报, 2017, 38(2): 127-148.
	ZHEN Ziyang, WANG Xinhua, JIANG Ju, et al. Research progress in guidance and control of automatic carrier landing of carrier-based aircraft[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(2): 127-148.
[3]	邓金来, 张志冰, 王家兴. 基于直接力的舰载机着舰控制技术研究[J]. 飞机设计, 2020, 40(2): 6-10.
	DENG Jinlai, ZHANG Zhibing, WANG Jiaxing. Reasarch on the control technology of carrier aircraft based on direct force[J]. Aircraft Design, 2020, 40(2): 6-10.
[4]	甄子洋. 舰载无人机自主着舰回收制导与控制研究进展[J]. 自动化学报, 2019, 45(4): 669-681.
	ZHEN Ziyang. Research development in autonomous carrier-landing/ship-recovery guidance and control of unmanned aerial vehicles[J]. Acta Automatica Sinica, 2019, 45(4): 669-681.
[5]	万辉, 张建, 刘秋红, 等. 美军航母担负的卫勤任务及保障需求[J]. 海军医学杂志, 2011, 32(1): 72-73.
[6]	曲浩, 段卓毅, 郭润兆. E-2D预警机与舰载战斗机着舰难度对比研究[J]. 西北工业大学学报, 2017, 35(增1):63-69.
	QU Hao, DUAN Zhuoyi, GUO Runzhao. Comparative study on the landing difficulty between E-2D early warning aircraft and carrier-based fighter aircraft[J]. Journal of Northwestern Polytechnical University, 2017, 35(S1): 63-69.
[7]	NATOPS landing signal officer reference manual (REV. B)[S], 1999.
[8]	LIN Ye, PANXing, HE Congjiao. Human reliability analysis in carrier-based aircraft recovery procedure based on CREAM[C]. 2015 First International Conference on Reliability Systems Engineering (ICRSE). IEEE, 2015: 1-6.
[9]	汪磊, 安佳宁, 赵新斌, 等. 基于QAR数据的飞行员个体超限风险精细化评价模型[J]. 中国安全科学学报, 2024, 34(8):35-42. doi: 10.16265/j.cnki.issn1003-3033.2024.08.1824
	WANG Lei, AN Jianing, ZHAO Xinbin, et al. Refined evaluation model for pilot's individual exceedance risk based on QAR data[J]. China Safety Science Journal, 2024, 34(8): 35-42. doi: 10.16265/j.cnki.issn1003-3033.2024.08.1824
[10]	李丽, 曹玉宽, 陈瑶, 等. 基于多生理信号的飞行警戒疲劳检测[J]. 中国安全科学学报, 2023, 33(2):225-232. doi: 10.16265/j.cnki.issn1003-3033.2023.02.0305
	LI Li, CAO Yukuan, CHEN Yao, et al. Flight alert fatigue detection based on multi-physiological signals[J]. China Safety Science Journal, 2023, 33(2): 225-232. doi: 10.16265/j.cnki.issn1003-3033.2023.02.0305
[11]	汪磊, 王朔, 高杉, 等. 面向特情的航线飞行员视觉注意分配研究[J]. 中国安全科学学报, 2023, 33(1):214-220. doi: 10.16265/j.cnki.issn1003-3033.2023.01.2167
	WANG Lei, WANG Shuo, GAO Shan, et al. A study on airline pilots visual attention allocation during emergency[J]. China Safety Science Journal, 2023, 33(1):214-220. doi: 10.16265/j.cnki.issn1003-3033.2023.01.2167
[12]	YANG Kun, TAO Liquan, BAI Jie. Assessment of flight crew errors based on THERP[J]. Procedia Engineering, 2014, 80: 49-58. doi: 10.1016/j.proeng.2014.09.059
[13]	GUO Yundong, SUN Youchao. Flight safety assessment based on an integrated human reliability quantification approach[J]. Plos One, 2020, 15(4): DOI:10.1371/journal.pone.0231391.
[14]	HIROSE T, SAWARAGI T, HORIGUCHI Y. Safety analysis of aviation flight-deck procedures using systemic accident model[J]. IFAC-Papers On Line, 2016, 49(19): 19-24.
[15]	SUN Guoqiang, ZENG Shengkui, GUO Jianbin, et al. A quantitative evaluation method of pilot errors during landing process of carrier-based aircraft[C]. 2015 First International Conference on Reliability Systems Engineering (ICRSE). IEEE, 2015: 1-6.
[16]	ZHAO Jianyu, SUN Guoqiang, ZENG Shengkui, et al. The reliability assessment of human systems interaction for aircraft carrier landing[J]. Journal of Mechanical Science and Technology, 2016, 30: 4465-4469. doi: 10.1007/s12206-016-0913-z
[17]	HOLLNAGEL E. Cognitive reliability and error analysis method (CREAM)[M]. Amsterdam: Elsevier, 1998: 246-248.
[18]	SHERIDAN T B. Human-robot interaction: status and challenges[J]. Human Factors, 2016, 58(4): 525-532. doi: 10.1177/0018720816644364 pmid: 27098262
[19]	FUJITA Y, HOLLNAGEL E. Failures without errors: quantification of context in HRA[J]. Reliability Engineering & System Safety, 2004, 83(2): 145-151. doi: 10.1016/j.ress.2003.09.006
[20]	JUST M A, CARPENTER P A. A theory of reading: from eye fixations to comprehension[J]. Psychological Review, 1980, 87(4): 329-354. pmid: 7413885
[21]	BEATTY J, LUCERO W B. Handbook of psychophysiology[M]. Cambridge: Cambridge University Press, 2007: 120-135.
[22]	LANG W, LANG M, KORNHUBER A, et al. Event-related EEG-spectra in a concept formation task[J]. Human Neurobiology, 1988, 6(4): 295-301. pmid: 3350710
[23]	DEIBER M P, MISSONNIER P, BERTRAND O, et al. Distinction between perceptual and attentional processing in working memory tasks: a study of phase-locked and induced oscillatory brain dynamics[J]. Journal of Cognitive Neuroscience, 2007, 19(1): 158-172. doi: 10.1162/jocn.2007.19.1.158
[24]	FAN Xiaoli, ZHAO Chaoyi, ZHANG Xin, et al. Assessment of mental workload based on multi-physiological signals[J]. Technology and Health Care, 2020, 28(S1): 67-80. doi: 10.3233/THC-209008 pmid: 32364145
[25]	ZHOU Biyun, CHEN Bo, SHI Huijuan, et al. SEMG-based fighter pilot muscle fatigue analysis and operation performance research[J]. Medicine in Novel Technology and Devices, 2022, 16:DOI:10.1016/j.medntd.2022.100189.
[26]	RUBIO S, DÍAZ E, MARTÍN J, et al. Evaluation of subjective mental workload: a comparison of SWAT, NASA-TLX, and workload profile methods[J]. Applied Psychology, 2004, 53(1): 61-86. doi: 10.1111/apps.2004.53.issue-1
[27]	VON J N, KRAUS J, ENGELN A, et al. A subjective one-item measure based on NASA-TLX to assess cognitive workload in driver-vehicle interaction[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 86: 210-225. doi: 10.1016/j.trf.2022.02.012

认知功能	失效模式	基本失效概率值
观察	观察目标错误	0.001
	错误辨识	0.007
	观察没有进行	0.007
解释	决策失误	0.01
	延迟解释	0.01
	诊断失误	0.02
计划	优先权不正确	0.01
计划	计划不当	0.01
执行	动作方式不正确	0.003
	动作时间不正确	0.003
	动作目标错误	0.000 5
	动作顺序错误	0.003
	动作遗漏	0.003

CPC因子名称	定义
着舰指导的完善性	驾驶舱内部的显示、告警信息等着舰指导信息的完善程度
着舰任务工作环境	不同的物理环境因素对于飞行员执行着舰任务的影响程度
人机界面显示元素的合理性	座舱内部人机界面设计方案对飞行员信息获取效率及人机交互能力的影响
着舰外界条件的复杂性	执行着舰任务时外界海况和天气条件对于飞行员的影响
需要同时关注的目标数	飞行员需同时关注的目标点数量的变化,对着舰任务产生的影响
着舰任务的可用时间	飞行员从开始进场到最终接触甲板所能利用的时间长短对着舰任务的影响
着舰任务时段	飞行员在一天中不同时段执行着舰任务对于安全性的影响
飞行员培训和经验的充分性	飞行员在执行着舰任务前的飞行经验及所接受的培训程度
着舰任务各成员的合作质量	飞行员与舰上机组人员间的沟通水平、交流质量对着舰任务的影响

任务阶段	飞行员动作	观察	解释	计划	执行
进场阶段	调整油门杆,维持速度	—	—	√	√
	操纵中央驾驶杆,保持着舰参考高度与迎角	—	—	√	√
	完成着舰相关检查,放下尾钩和起落架,切断武器开关	—	√	—	√
	监控飞机速度、高度、迎角等飞行参数	√	—	—	—
任务阶段	飞行员动作	观察	解释	计划	执行
下滑阶段	横向操纵中央驾驶杆,转弯	—	—	√	√
	监控姿态、航向等	√	—	—	—
	判断着舰点距离	—	√	—	—
	与着舰指挥官沟通,沿正确下滑道下滑	√	—	—	√
	观察菲涅尔透镜,保持飞机高度	√		√	—
	控制驾驶杆对准着舰跑道中线	—	—	√	√
拦阻阶段	接触航母甲板后,将油门推到最大,勾住拦阻索,关闭发动机,完全制动	—	—	—	√
拦阻阶段	若接收复飞指令,保持油门最大再次起飞	—	√	√	√

CPC因子	环境因素设置
着舰指导的完善性	告警形式1(告警文字闪烁)
	告警形式2(告警文字常亮)
	告警形式3(告警文字闪烁伴有语音提示)
	告警形式4(仅有语音提示)
着舰任务工作环境	低温(座舱温度20 ℃)
	常温(座舱温度25 ℃)
	高温(座舱温度30 ℃)
	有噪声
	无噪声
人机界面显示元素的合理性	小号文字、图符
	正常文字、图符
	大号文字、图符
着舰条件的复杂性	平静海面
	轻微波浪、小雨
	大海浪、大雨
需要同时关注的目标数	正常模式
需要同时关注的目标数	简化模式(仅保留关键数值)
着舰任务时段	日间航行
着舰任务时段	夜间航行

认知功能	认知功能定义	负荷种类
观察	飞行员对环境、任务信息等的观察和监控能力	视觉负荷
解释	飞行员对观察到的信息进行分析和理解的过程	脑力负荷
计划	飞行员根据解释的信息制定行动策略或应对方案	心理负荷
执行	飞行员将规划好的策略付诸实施的过程	操作负荷