Enter your email address
Submit
Write your message
Volume 9, Issue 3 (Iranian Journal of Ergonomics 2021)
Iran J Ergon 2021, 9(3): 84-103 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Atashfeshan N, Saidi-Mehrabad M, Razavi H. Identification and Evaluation of Fault in Human-Machine Interactive System using CREAM Technique and Fault Tree Analysis. Iran J Ergon 2021; 9 (3) :84-103
URL: http://journal.iehfs.ir/article-1-822-en.html
URL: http://journal.iehfs.ir/article-1-822-en.html
1- PhD Candidate, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
2- Professor, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran. , mehrabad@iust.ac.ir
3- Associate Professor, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2- Professor, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran. , mehrabad@iust.ac.ir
3- Associate Professor, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract: (4256 Views)
Background and Objectives: Despite contribution to catastrophic accidents, human errors have been generally ignored in the design of human-machine (HM) systems and the determination of the level of automation (LOA). This paper aims to develop a method to estimate the level of automation in the early stage of the design phase considering both human and machine performance.
Methods: A quantitative method is used to evaluate the performance of the whole human-machine system by the human-in-the-loop fault tree analysis while a qualitative and cross-sectional method is used to estimate human errors using the CREAM technique. The data are collected from real cases that happened in the control room of the Ferdowsi power plant.
Results: Full automatic option with an average error of 0.013 had the lowest error rate, i.e. 1/8 of the error rate of the manual design. In addition, the CREAM analysis showed that the control room operators were not satisfied with the availability of procedures and Man-Machine Interface and operational support in general. Thus, on average, the reliability of the manual design is less than the reliability of the automatic setting.
Conclusion: High machine reliability has led to the fact that the fully automatic design would be one of the best design choices for human-machine systems. However, based on the previous studies, high automation may have some human-out-of-the-loop shortcomings. Thus, this study proposed solutions to overcome these disadvantages based on the importance of the control parameters or the essence of human involvement in some decision-making and execution tasks.
Methods: A quantitative method is used to evaluate the performance of the whole human-machine system by the human-in-the-loop fault tree analysis while a qualitative and cross-sectional method is used to estimate human errors using the CREAM technique. The data are collected from real cases that happened in the control room of the Ferdowsi power plant.
Results: Full automatic option with an average error of 0.013 had the lowest error rate, i.e. 1/8 of the error rate of the manual design. In addition, the CREAM analysis showed that the control room operators were not satisfied with the availability of procedures and Man-Machine Interface and operational support in general. Thus, on average, the reliability of the manual design is less than the reliability of the automatic setting.
Conclusion: High machine reliability has led to the fact that the fully automatic design would be one of the best design choices for human-machine systems. However, based on the previous studies, high automation may have some human-out-of-the-loop shortcomings. Thus, this study proposed solutions to overcome these disadvantages based on the importance of the control parameters or the essence of human involvement in some decision-making and execution tasks.
Keywords: Human-Machine Systems, Function Allocation, Human Reliability, Human-in-the-loop fault tree analysis, Human factors
Type of Study: Research |
Subject:
Other Cases
Received: 2021/06/4 | Accepted: 2022/01/30 | ePublished: 2022/01/30
Received: 2021/06/4 | Accepted: 2022/01/30 | ePublished: 2022/01/30
References
1. Hollnagel E. Cognitive reliability and error analysis method (CREAM): Elsevier; 1998.
2. Janssen CP, Donker SF, Brumby DP, Kun AL. History and future of human-automation interaction. International Journal of Human-Computer Studies. 2019;131:99-107. [DOI]
3. Parasuraman R. Designing automation for human use: empirical studies and quantitative models. Ergonomics. 2000;43(7):931-51. [DOI]
4. Hancock PA. Human Performance in Automated and Autonomous Systems, Two-Volume Set. 2019.
5. Fitts PM. Human engineering for an effective air-navigation and traffic-control system. 1951.
6. Pacaux-Lemoine M-P, Trentesaux D, Rey GZ, Millot P. Designing intelligent manufacturing systems through Human-Machine Cooperation principles: A human-centered approach. Computers & Industrial Engineering. 2017;111:581-95. [DOI]
7. Schmitt K. Automations influence on nuclear power plants: a look at three accidents and how automation played a role. Work. 2012;41(Supplement 1):4545-51. [DOI]
8. Johnson AW, Oman CM, Sheridan TB, Duda KR, editors. Dynamic task allocation in operational systems: Issues, gaps, and recommendations. 2014 IEEE Aerospace Conference; 2014: IEEE. [DOI]
9. Canellas M, Haga R. Unsafe at any level. Communications of the ACM. 2020;63(3):31-4. [DOI]
10. Leggett T. Who is to blame for 'self-driving car' deaths? 2018 [Available from: https://www.bbc.com/news/business-44159581.
11. Kaber DB, Endsley MR. Out‐of‐the‐loop performance problems and the use of intermediate levels of automation for improved control system functioning and safety. Process Safety Progress. 1997;16(3):126-31. [DOI]
12. Porthin M, Liinasuo M, Kling T. Effects of digitalization of nuclear power plant control rooms on human reliability analysis–A review. Reliability Engineering & System Safety. 2020;194:106415. [DOI]
13. Hogenboom S, Rokseth B, Vinnem JE, Utne IB. Human reliability and the impact of control function allocation in the design of dynamic positioning systems. Reliability Engineering & System Safety. 2020;194:106340. [DOI]
14. Parasuraman R, Riley V. Humans and automation: Use, misuse, disuse, abuse. Human factors. 1997;39(2):230-53. [DOI]
15. Taylor JR. Statistics of design error in the process industries. Safety science. 2007;45(1-2):61-73. [DOI]
16. Ashrafi M, Davoudpour H, Khodakarami V. A Bayesian network to ease knowledge acquisition of causal dependence in CREAM: application of recursive noisy‐OR gates. Quality and Reliability Engineering International. 2017;33(3):479-91. [DOI]
17. Iqbal MU, Srinivasan R. Simulator based performance metrics to estimate reliability of control room operators. Journal of Loss Prevention in the Process Industries. 2018;56:524-30. [DOI]
18. Parasuraman R, Sheridan TB, Wickens CD. A model for types and levels of human interaction with automation. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans. 2000;30(3):286-97. [DOI]
19. Wang P, Fang W, Guo B, Bao H, editors. Apply petri nets to human performance and workload prediction under multitask. International Conference on Applied Human Factors and Ergonomics; 2017: Springer.
20. Balfe N, Sharples S, Wilson JR. Impact of automation: Measurement of performance, workload and behaviour in a complex control environment. Applied ergonomics. 2015;47:52-64. [DOI]
21. Johnson AW, Duda KR, Sheridan TB, Oman CM. A closed-loop model of operator visual attention, situation awareness, and performance across automation mode transitions. Human factors. 2017;59(2):229-41. [DOI]
22. Li P-c, Zhang L, Dai L-c, Li X-F. Study on operator’s SA reliability in digital NPPs. Part 3: A quantitative assessment method. Annals of Nuclear Energy. 2017;109:82-91. [DOI]
23. Jiao J, Zhou F, Gebraeel NZ, Duffy V. Towards augmenting cyber-physical-human collaborative cognition for human-automation interaction in complex manufacturing and operational environments. International Journal of Production Research. 2020;58.111-5089(16). [DOI]
24. Schaefer KE, Chen JY, Szalma JL, Hancock PA. A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human factors. 2016;58(3):377-400. [DOI]
25. Ramos MA, Thieme CA, Utne IB, Mosleh A. A generic approach to analysing failures in human–System interaction in autonomy. Safety science. 2020;129:104808. [DOI]
26. Simonsen E, Osvalder A-L. Categories of measures to guide choice of human factors methods for nuclear power plant control room evaluation. Safety science. 2018;102:101-9. [DOI]
27. Zoaktafi M, Zakerian SA, Choobine A, Nematolahi S. Validation of a Task Demand Measure (VACP) for Predicting Mental Workloads of Control Room Operators (A Case Study: Pars Combined Cycle Power Plant). Iranian Journal of Ergonomics. 2016;4(3):26-32.
28. Blischke WR, Murthy DP. Reliability: modeling, prediction, and optimization: John Wiley & Sons; 2011.
29. Signoret J-P, Leroy A. Fault Tree Analysis (FTA). Reliability Assessment of Safety and Production Systems: Springer; 2021. p. 209-25.
30. Doytchev DE, Szwillus G. Combining task analysis and fault tree analysis for accident and incident analysis: a case study from Bulgaria. Accident Analysis & Prevention. 2009;41(6):1172-9. [DOI]
31. Ruijters E, Stoelinga M. Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer science review. 2015;15:29-62. [DOI]
32. Yanjun Z, Youchao S, editors. Safety Risk Assessment of Human-Machine Interaction Behavior in Cockpit. 2014 Seventh International Symposium on Computational Intelligence and Design; 2014: IEEE. [DOI]
33. Sheridan TB, Verplank WL. Human and computer control of undersea teleoperators. Massachusetts Inst of Tech Cambridge Man-Machine Systems Lab; 1978.
34. Kim MC, Seong PH, Hollnagel E. A probabilistic approach for determining the control mode in CREAM. Reliability Engineering & System Safety. 2006;91(2):191-9. [DOI]
35. Konstandinidou M, Nivolianitou Z, Kiranoudis C, Markatos N. A fuzzy modeling application of CREAM methodology for human reliability analysis. Reliability Engineering & System Safety. 2006;91(6):706-16. [DOI]
36. Shirali GA, Hosseinzadeh T, Kalhori SRN. Modifying a method for human reliability assessment based on CREAM-BN: A case study in control room of a petrochemical plant. MethodsX. 2019;6:300-15. [DOI]
37. Mohammadfam I, Movafagh M, Soltanian A, Salavati M, Bashirian S. Identification and evaluation of human errors among the nurses of coronary care unit using CREAM techniques. Iranian Journal of Ergonomics. 2014;2(1):27-35.
38. Wickens CD, Dixon SR. The benefits of imperfect diagnostic automation: A synthesis of the literature. Theoretical Issues in Ergonomics Science. 2007;8(3):201-12. [DOI]
39. Yang Z, Bonsall S, Wall A, Wang J, Usman M. A modified CREAM to human reliability quantification in marine engineering. Ocean engineering. 2013;58:293-303. [DOI:] [DOI]
40. Salvendy G, Karwowski W. Handbook Of Human Factors and Ergonomics. 5 ed: John Wiley & Sons; 2021.
41. Price H, Pulliam R. The allocation of functions in man-machine systems. 1982.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Copyright Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
