HEP quantification strategy based on modified CREAM

doi:10.21629/JSEE.2019.04.18

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (4): 815-822.doi: 10.21629/JSEE.2019.04.18

• Reliability • Previous Articles

HEP quantification strategy based on modified CREAM

Xing PAN*(), Huixiong WANG(), Ye LIN(), Tun LIU(), Xiaoxia WANG()

School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China

Received:2018-01-12 Online:2019-08-01 Published:2019-09-01
Contact: Xing PAN E-mail:panxing@buaa.edu.cn;wanghuixiong@buaa.edu.cn;yelin@bu.edu;liu_tun@163.com;langshixuan@foxmail.com
About author:PAN Xing was born in 1979. He received his B.S. degree in mechanical engineering, and Ph.D. degree in systems engineering from Beihang University, Beijing, China, in 2000 and 2005 respectively. From 2005 to 2009, he was an assistant professor with the School of Reliability and Systems Engineering, Beihang University, Beijing, China. Since 2009, he has been an associate professor. From 2012 to 2013, he was a visiting scholar at the Department of Systems and Industrial Engineering, University of Arizona, Tucson, USA. He is currently an associate professor with the School of Reliability and Systems Engineering of Beihang University. His research interests include reliability engineering, systems engineering, and system risk analysis. E-mail:panxing@buaa.edu.cn|WANG Huixiong was born in 1995. He received his B.S. degree in safety science from School of Reliability and Systems Engineering, Beihang University, in 2018. He is currently a master student in the same school. His interests of research include systems engineering and complex network. E-mail:wanghuixiong@buaa.edu.cn|LIN Ye was born in 1993. She received her B.S. degree in aerospace engineering, and M.S. degree in systems engineering from Beihang University, Beijing, China, in 2015 and 2017 respectively. Currently, She is a Ph.D. candidate in systems engineering from Boston University, MA, USA. Her research interests include reliability engineering and systems engineering. E-mail:yelin@bu.edu|LIU Tun was born in 1996. He received his B.S. degree in mechanical science and engineering from Jilin University, Jilin, China, in 2017. He is currently pursuing his M.S. degree in reliability and systems engineering with the School of Reliability and Systems Engineering, Beihang University, Beijing, China. His research interests include human reliability analysis, reliability engineering, and system risk analysis. E-mail:liu_tun@163.com|WANG Xiaoxia was born in 1994. She received her B.S. degree in safety engineering from China University of Mining and Technology, Jiangsu, China. She is currently pursuing her M.S. degree in reliability and systems engineering with the School of Reliability and Systems Engineering, Beihang University. Her research focus is human reliability, including quantification of human reliability for individuals and crews. E-mail:langshixuan@foxmail.com
Supported by:
the National Natural Science Foundation of China(71171008);the National Natural Science Foundation of China(71571004);This work was supported by the National Natural Science Foundation of China (71171008; 71571004)

Abstract

Abstract:

The accurate quantification of human error probability (HEP) has long been a main pursuit for most human reliability analysis (HRA) methods. This paper proposes a strategy to analyze and predict human error on the basis of a further modification of the existing cognitive reliability and error analysis method (CREAM) in HRA. Through providing a broader definition for the key parameters used in the quantification method, this paper produces a relatively flexible strategy to determine the nominal HEP (HEP₀). Basing on this strategy, the actual quantification of HEP in CREAM is able to be applied to more verified conditions. To prove the validity of the method proposed, a case study of spacecraft launch is also introduced, in which the prediction derived from the method is proved consistent with real field data.

Key words: human reliability, cognitive reliability and error analysis method (CREAM), context influence, nominal human error probability (HEP₀)

Xing PAN, Huixiong WANG, Ye LIN, Tun LIU, Xiaoxia WANG. HEP quantification strategy based on modified CREAM[J]. Journal of Systems Engineering and Electronics, 2019, 30(4): 815-822.

Figures/Tables 6

Fig 1

Table 1

Fig 2

Table 2

Fig 3

Table 3

Evaluations on CPCs in task context"

CPC	Task context	Level	Effect
Adequacy of organization	As the commander holds the top priority in the crew group and takes full responsibility for all the instructions in the fueling process, the relationship in the organization is not well-balanced.	Inefficient	R
Working conditions	The commander has to communicate with different operators from a far distance without knowing the latest situation and the conditions in this environment can be considered as stressful and taxing.	Incompatible	R
Adequacy of MMI and operational support	The keyboard buttons are too small and the commander has inadvertently touched the button in the commanding task.	Inadequate	R
Availability of procedures/plans	The whole fueling process has been fully informed to all the crew members and the commander is familiar with the processes.	Adequate	NS
Number of simultaneous goals	The commander has to monitor the performance of all the crew members while giving correct instruction at the proper time. Also, if there is any emergency, the commander has to stop the whole fueling process.	More than capacity	R
Available time	The commander is working in extremely high-pressure and in most cases. He or she may not have any chance to correct the mistake in instruction as the time schedule is compact.	Inadequate	R
Time of day (circadian rhythm)	The exact time for the fueling task corresponds to the spacecraft launch and the command task can be at any time of the day; thus, the commander may not be at his or her favorite circadian rhythm.	Unadjusted	R
Adequacy of training and experience	The command needs to take comprehensive training to acquire the qualification for performing the command task and most commanders have years of experiences.	Adequate	I
Crew collaboration quality	The crew members are capable of keeping communicating and cooperating with each in the fueling task.	Adequate	NS

Table 3

References 16

1	HOLLNAGEL E. Cognitive reliability and error analysis method (CREAM). Amsterdam: Elsevier, 1998.
2	PAN X, LIN Y, HE C. A review of cognitive models in human reliability analysis. Quality and Reliability Engineering International, 2016, 33 (7): 1299- 1316.
3	YANG Z, BONSALL S, WALL A, et al. A modified CREAM to human reliability quantification in marine engineering. Ocean Engineering, 2013, 58, 293- 303. doi: 10.1016/j.oceaneng.2012.11.003
4	UNG S T. A weighted CREAM model for maritime human reliability analysis. Safety Science, 2015, 72, 144- 152. doi: 10.1016/j.ssci.2014.08.012
5	MU L, XIAO B, YUAN Z, et al. The research of CREAM prediction analysis method based on BP neural network under dynamic context. Proc. of the 1st International Conference on Reliability Systems Engineering, 2015: 1-6.
6	CALHOUN J, SAVOIE C, RANDOLPH-GIPS M, et al. Human reliability analysis in spaceflight applications, Part 2:modified CREAM for spaceflight. Quality and Reliability Engineering International, 2014, 30 (1): 3- 12.
7	DEETER J L. Human reliability analysis in healthcare: application of the cognitive reliability and error analysis method (CREAM) in a hospital setting. https://search.proquest.com/docview/1024285130.
8	HE X, WANG Y, SHEN Z, et al. A simplified CREAM prospective quantification process and its application. Reliability Engineering & System Safety, 2008, 93 (2): 298- 306.
9	SUN Z, LI Z, GONG E, et al. Estimating human error probability using a modified CREAM. Reliability Engineering & System Safety, 2012, 100, 28- 32.
10	LAUMANN K. Criteria for qualitative methods in human reliability analysis. Reliability Engineering & System Safety, 2018, DOI: 10.1016/j.ress.2018.07.001.
11	PAN X, WU Z. Performance shaping factors in the human error probability modification of human reliability analysis. International Journal of Occupational Safety and Ergonomics, 2018, 1- 13.
12	CHANDLER F, CHANG Y, MOSLEH A, et al. Human reliability analysis methods: selection guidance for NASA. Washington, DC: NASA Office of Safety and Mission Assurance, 2006.
13	FOYLE D C, HOOEY B L. Using human performance modeling in aviation. Boca Raton: CRC Press, 2007.
14	SALOTTI J M, SUHIR E. Manned missions to Mars:minimizing risks of failure. Acta Astronautica, 2014, 93, 148- 161. doi: 10.1016/j.actaastro.2013.07.005
15	ILYIN E A, KHOLIN S F, GUSHIN V I, et al. Human factor in manned Mars mission. Advances in Space Research, 1992, 12 (1): 271- 279. doi: 10.1016/0273-1177(92)90295-9
16	AMBATI P R, PADHI R. Robust auto-landing of fixed-wing UAVs using neuro-adaptive design. Control Engineering Practice, 2017, 60, 218- 232. doi: 10.1016/j.conengprac.2016.03.017

Control mode	HEP interval
Strategic	(0.000 05, 0.001 00)
Tactical	(0.001 00, 0.100 00)
Opportunistic	(0.010 00, 0.500 00)
Scrambled	(0.100 00, 1.000 00)

∑ Improved	∑ Reduced
∑ Improved	0	1	2	3	4	5	6	7	8	9
0	-0.006 65	-0.011 53	-0.019 99	-0.034 66	-0.060 09	-0.104 16	-0.180 57	-0.313 04	-0.542 68	-
1	-0.003 28	-0.005 68	-0.009 86	-0.017 08	-0.029 62	-0.051 34	-0.089 02	-0.154 31	-	-
2	-0.001 62	-0.002 80	-0.004 86	-0.008 42	-0.014 60	-0.025 31	-0.043 88	-	-	-
3	-0.000 80	-0.001 38	-0.002 39	-0.004 15	-0.007 20	-0.012 48	-	-	-	-
4	-0.000 39	-0.000 68	-0.001 18	-0.002 05	-0.003 55	-	-	-	-	-
5	-0.000 19	-0.000 34	-0.000 58	-0.001 01	-	-	-	-	-	-
6	-0.000 10	-0.000 17	-0.000 29	-	-	-	-	-	-	-
7	-0.000 05	-0.000 08	-	-	-	-	-	-	-	-

[1]	Zhiqiang Sun, Erling Gong, Zhengyi Li, Yingjie Jiang, and Hongwei Xie. Bayesian estimator of human error probability based on human performance data [J]. Journal of Systems Engineering and Electronics, 2013, 24(2): 242-249.
[2]	Sun Zhiqiang, Xie Hongwei, Shi Xiujian & Liu Fengqiang. Engineering approach for human error probability quantification [J]. Journal of Systems Engineering and Electronics, 2009, 20(5): 1144-1152.

HEP quantification strategy based on modified CREAM

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 6

References 16

Related Articles 2

Recommended Articles

Metrics

Comments