Human Reliability Analysis: A review of the state of the art

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an assessment of given control mode, as highlighted by figures provided in table below (Tab.1). As can be seen by contents of table, each of specified control modes has an individual reliability level. In extended CREAM version, control modes play the role of a weighting factor which scales a nominal failure probability associated to a given cognitive function failure. This version of CREAM is intended to be used for purposes of a more in depth analysis of human interactions. TABLE I. Control mode RELIABILITY INTERVAL Reliability Interval strategic 0.5 E-5 of basic CREAM method is to use it as a screening technique with the aim of identifying processes which require a deeper level of analysis; this analysis may then be carried out by the extended CREAM method. 3.Specification of Initiating Events. When using basic CREAM method, a task analysis is conducted prior to further assessment. CPCs are assessed according to descriptors, given in the table below (Tab.2), in order to judge their expected effect on performance. TABLE 2. COMMON PERFORMANCE CONDITIONS (CPCS) CPC Descriptors Expected effect Adequacy of Very efficient Improved organisation Efficient Not significant Working conditions Adequacy of MMI and operational support Availability of procedures/ Inefficient Deficient Advantageous Compatible Incompatible Supportive Adequate Tolerable Inappropriate Appropriate IRACST- International Journal of Research in Management & Technology (IJRMT), ISSN: 2249-9563 Vol. 2, No. 1, 2012 CPC Descriptors Expected effect plans Reduced Reduced Improved Not significant Reduced Improved Not significant Not significant Reduced Improved Number of simultaneous goals Accettable Inappropriate Fewer than capacity Matching current capacity More than capacity Not significant Reduced Not significant Not significant Reduced Available Adequate Improved time Temporarily Not significant inadequate Continuously Reduced inadequate Time of day Day-time (adjusted) Not significant Adequacy of training and expertise Crew collaboration quality Night-time (unadjusted) Adequate, high experience Adequate, limited experience Inadequate Very efficient Efficient Inefficient Deficient Reduced Improved Not significant Reduced Improved Not significant Not significant Reduced 4.Error prediction. Assessments of the CPCs then require to be adjusted according to some specified rules in order to take account of synergistic effects. Matrix above would be considered in the context of the situation under assessment and by this means the previously considered initiating events are reviewed with respect to how they could potentially lead to occurrence of an error. Rows of matrix identify the possible outcomes while the columns show the precursors. Analyst then has task of identifying the columns for which all the rows have been similarly classified into the same group according to column headings. Predicting the possible outcomes for each of rows should be done until there are no remaining possible paths. Each of identified errors requires to be noted along with causes and the outcomes. 5.Determination of control mode. Finally, a simple count is performed of the number of CPCs that are causing an improvement in reliability and those which are reducing it. On the basis of this number the probable control mode is determined. The main advantages of this methodology are: the technique uses the same principles for retrospective and 40
predictive analyses; the approach is very concise, wellstructured and follows a well laid out system of procedure; the technique allows for the direct quantification of HEP; it also allows evaluator using the CREAM method to specifically tailor the use of technique to contextual situation [30]. Instead, the main criticism are: this technique requires a high level of resource use, including lengthy time periods for completion; CREAM also requires an initial expertise in field of human factors in order to use technique successfully and may therefore appear rather complex for an inexperienced user; CREAM does not put forth potential means by which identified errors can be reduced; time required for application is very lengthy. REFERENCES [1] F. De Felice, A. Petrillo. Methodological Approach for Performing Human Reliability and Error Analysis in Railway Transportation System. International Journal of Engineering and Technology Vol.3 (5), 2011, 341-353. [2] F. 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Kariuki and K. Lowe (2007) Integrating human factors into process analysis.Reliability Engineering and System Safety.921764-1773. [9] J. Ren, I Jenkinson, J. Wang, D.L. Xu, and J.B. Yang (2008) A methodology to model causal relationships in offshore safety assessment focusing on human and organisational factors.Journal of Safety Research.3987-100. [10] R.L. Helmreich (2000) On error management: lessons from aviation. British Medical Journal.320(7237) 781–785. [11] E. Hollnagel (1993) Human Reliability Analysis: Context and Control. Academic Press, London. [12] E. Grandjean (1980) Fitting the Task to the Man. Taylor and Francis, London. [13] D. Embrey (2000) Data Collection Systems.Human Reliability Associates, Wigan. IRACST- International Journal of Research in Management & Technology (IJRMT), ISSN: 2249-9563 Vol. 2, No. 1, 2012 [14] M. Lyons, A. Sally, M. Woloshynowych, C. Vincent (2004) Human Reliability Analysis in healthcare: A review of technique.International Journal of Risk &Safety in medicine. [15] E. Hollnagel (1998) Cognitive Reliability and Error Analysis Method – CREAM. Elsevier Science, Oxford. [16] E. Hollnagel (2000) Looking for errors of omission and commission or The Hunting of the Snarkrevisited.Reliability Engineering and System Safety. 68 135–145. [17] E. Doughty (1990) Human reliability analysis - where shouldst thou turnReliability Engineering and System Safety. 29(3) 283-299. [18] R.G. Lord, P.E. Levy (1994) Moving from cognition to action – a control theory perspective. Applied Psychology - An International Review (Psychologieappliquee - Revue Internationale). [19] J. Wreatall (1982)Operator action trees. An approach to quantifying operator error probability during accident sequences, NUS-4159. NUS Corporation, San Diego (California). [20] A.D. Swain (1989) Comparative evaluation of methods for human reliability analysis,(GRS-71). Garching, FRG: GesellschaftfürReaktorsicherheit. [21] A.D. Swain & H.E. Guttman (1983) Handbook of human reliability analysis with emphasis on nuclear power plant applications, NUAREG CR-1278.NRC, Washington, DC. [22] B. Kirwan (1996) The validation of three human reliability quantification techniques - THERP, HEART, JHEDI: Part I -- technique descriptions and validation issues. Applied Ergonomics. 27(6) 359-373. [23] G.C Bello and C. Columbari (1980) The human factors in risk analyses of process plants: the control room operator model, TESEO. Reliability Engineering.13-14. [24] P.C. Humphreys, Human Reliability Assessor’s Guide. 1995, Human Factors in Reliability Group, SRD Association. [25] G.W. Hannaman, A.J. Spurgin, and Y.D. Lukic, Human cognitive reliability model for PRA analysis. Draft Report NUS-4531, EPRI Project RP2170-3. 1984, Electric Power and Research Institute: Palo Alto, CA. [26] J. Rasmussen (1983) Skills, rules, knowledge; signals, signs and symbols and other distinctions in human performance models.IEEE Transactions on Systems, Man and Cybernetics.SMC-13(3). [27] M., Barriere, D. Bley, S. Cooper, J. Forester, A. Kolaczkowski, W. Luckas, G. Parry, A. Ramey-Smith, C. Thompson, D. Whitehead, and J. Wreathall, NUREG-1624: Technical basis and implementation guidelines for a technique for human event analysis (ATHEANA). 2000, US Nuclear Regulatory Commission. [28] J. Forester, D. Bley, S. Cooper, E. Lois, N. Siu, A Kolaczkowski, and J. Wreathall, (2004) Expert elicitation approach for performing ATHEANA quantification.. Reliability Engineering and System Safety.83(2) 207-220. [29] J. Forester, A. Ramey-Smith, D. Bley, A. Kolaczkowski, S. Cooper, and J. Wreathall, SAND--98-1928C: Discussion of comments from a peer review of a technique for human event analysis (ATHEANA), .1998, Sandia Laboratory [30] P. Salmon, N.A. Stanton and G. 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Human Reliability Analysis: A review of the state of the art

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