Screening, sensitivity, and uncertainty for the CREAM method of Human Reliability Analysis

被引:25
|
作者
Bedford, Tim [1 ]
Bayley, Clare [2 ]
Revie, Matthew [1 ]
机构
[1] Univ Strathclyde, Glasgow G1 1QE, Lanark, Scotland
[2] Univ Manchester, Manchester, Lancs, England
基金
英国工程与自然科学研究理事会;
关键词
Human Reliability; Human failure; CREAM; Sensitivity; Screening; Principles of screening methods; QUANTIFICATION;
D O I
10.1016/j.ress.2013.02.011
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper reports a sensitivity analysis of the Cognitive Reliability and Error Analysis Method for Human Reliability Analysis. We consider three different aspects: the difference between the outputs of the Basic and Extended methods, on the same HRA scenario; the variability in outputs through the choices made for common performance conditions (CPCs); and the variability in outputs through the assignment of choices for cognitive function failures (CFFs). We discuss the problem of interpreting categories when applying the method, compare its quantitative structure to that of first generation methods and discuss also how dependence is modelled with the approach. We show that the control mode intervals used in the Basic method are too narrow to be consistent with the Extended method. This motivates a new screening method that gives improved accuracy with respect to the Basic method, in the sense that (on average) halves the uncertainty associated with the Basic method. We make some observations on the design of a screening method that are generally applicable in Risk Analysis. Finally, we propose a new method of combining CPC weights with nominal probabilities so that the calculated probabilities are always in range (i.e. between 0 and 1), while satisfying sensible properties that are consistent with the overall CREAM method. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:100 / 110
页数:11
相关论文
共 50 条
  • [21] A modified CREAM to human reliability quantification in marine engineering
    Yang, Z. L.
    Bonsall, S.
    Wall, A.
    Wang, J.
    Usman, M.
    OCEAN ENGINEERING, 2013, 58 : 293 - 303
  • [22] Reliability sensitivity by method of moments
    Lu, Zhenzhou
    Song, Jun
    Song, Shufang
    Yue, Zhufeng
    Wang, Jian
    APPLIED MATHEMATICAL MODELLING, 2010, 34 (10) : 2860 - 2871
  • [23] An HFM-CREAM model for the assessment of human reliability and quantification
    Lin, Chuan
    Xu, Qi Feng
    Huang, Yi Fan
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2022, 38 (05) : 2372 - 2387
  • [24] Sequential Optimization and Mixed Uncertainty Analysis Method for Reliability-Based Optimization
    Yao, Wen
    Chen, Xiaoqian
    Huang, Yiyong
    Gurdal, Zafer
    van Tooren, Michel
    AIAA JOURNAL, 2013, 51 (09) : 2266 - 2277
  • [25] Reliability sensitivity method by line sampling
    Lu, Zhenzhou
    Song, Shufang
    Yue, Zhufeng
    Wang, Jian
    STRUCTURAL SAFETY, 2008, 30 (06) : 517 - 532
  • [26] A modified human reliability analysis method for the estimation of human error probability in the offloading operations at oil terminals
    Zhang, Renyou
    Tan, Henry
    Afzal, Waheed
    PROCESS SAFETY PROGRESS, 2021, 40 (03) : 84 - 92
  • [27] A method of human reliability analysis and quantification for space missions based on a Bayesian network and the cognitive reliability and error analysis method
    Chen, Jiayu
    Zhou, Dong
    Lyu, Chuan
    Zhu, Xinv
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2018, 34 (05) : 912 - 927
  • [28] Reliability analysis under epistemic uncertainty
    Nannapaneni, Saideep
    Mahadevan, Sankaran
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2016, 155 : 9 - 20
  • [29] A Study on Uncertainty Analysis of Fatigue Reliability
    Tang, Yong
    Li, Mingzhang
    Liu, Ningbo
    Zhu, Shun-Peng
    Huang, Hong-Zhong
    2012 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING (ICQR2MSE), 2012, : 909 - 914
  • [30] New improved CREAM model for human reliability analysis using a linguistic D number-based hybrid decision making approach
    Shi, Hua
    Wang, Jing-Hui
    Zhang, Ling
    Liu, Hu-Chen
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 120