Estimating Human Error Probability using a modified CREAM

Sun, Zhiqiang; Li, Zhengyi; Gong, Erling; Xie, Hongwei

doi:10.1016/j.ress.2011.12.017

Cited by 48 publications

(32 citation statements)

References 10 publications

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“…The cognitive model used in the CREAM is the Contextual Control Model ("CoCoM") (Sun et al, 2012) which considers separately the connection of cognitive functions ("competence") and the cognitive processes that produce the evolution ("control").…”

Section: Cognitive Reliability and Error Analysis Methods (Cream): Tramentioning

confidence: 99%

The Management of Uncertainty: Model for Evaluation of Human Error Probability in Railway System

Lombardi¹

2014

American Journal of Applied Sciences

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The methodology of quantitative risk assessment in use in the railway industry refers the RAMS approach, as expected in EN 50126, to define the failure of the system. From the probability of failure follows the evaluation of the failure consequence, the check of the accident occurrence and finally, the analysis of the achievable accident scenarios to quantify the damage as result thereof. In this approach, the central concept is the failure phenomenon, i.e., the deviation of the proper performance of a function by a system component. The case-study is referred to the maintenance procedure, necessary to ensure safe use of the railway infrastructure. The homogeneous treatment of human behaviour in the proposed risk model involves the definition of the function of reliability (or failure) of "human component" since the maintenance procedure of railway system shows a high probability of human error. The paper analyzes a series of control checks for back analysis testing of accident occurrences and highlights the opportunity to perform quantitative risk analysis comparing alternative designs of railway system by analogical analysis methods. In this analysis the evaluation of human error according to combined techniques of risk analysis and Ishikawa's theory is relevant because in the railway industry organized procedures of good practice usually have been employed.

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Section: Cognitive Reliability and Error Analysis Methods (Cream): Tramentioning

confidence: 99%

The Management of Uncertainty: Model for Evaluation of Human Error Probability in Railway System

Lombardi¹

2014

American Journal of Applied Sciences

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“…We apply the approach proposed in to estimate the HEP point estimation in terms of the combine number of positive and negative effects of CPCs:

U ((), H E P) = 7.07 \times 10^{prefix- 3} exp ([], prefix- 4.9517 ((), \frac{true \sum italicpositive effect}{max ((), true \sum italicpositive effect)} - \frac{true \sum italicnegative effect}{max ((), true \sum italicnegative effect)}))

where

\frac{true \sum italicpositive effect}{max ((), true \sum italicpositive effect)}

and

\frac{true \sum italicnegative effect}{max ((), true \sum italicnegative effect)}

are normalized number of positive and negative effect, respectively and max(∑ positive effect ) = 7, max(∑ negative effect ) = 9.…”

Section: Application In Estimation Of Hepmentioning

confidence: 99%

A Bayesian Network to Ease Knowledge Acquisition of Causal Dependence in CREAM: Application of Recursive Noisy‐OR Gates

Ashrafi

Davoudpour

Khodakarami

2016

Quality & Reliability Eng

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Cognitive Reliability and Error Analysis Method (CREAM) is a common Human Reliability Analysis (HRA) method of second generation. In this paper, to improve the capabilities of CREAM, we propose a probabilistic method based on Bayesian Network (BN) to determine control mode and quantify Human Error Probability (HEP). The BN development process is described in a four‐phase methodology including (i) definition of the nodes and their states; (ii) building the graphical structure; (iii) quantification of BN through assessment of the Conditional Probability Tables (CPT) values and (iv) model validation. Intractability of knowledge acquisition of large CPTs is the most significant limitation of existing BN model of CREAM. So, the main contribution of this paper lies in its application of Recursive Noisy‐OR (RN‐OR) gate to treat large CPTs assessment and ease knowledge acquisition. RN‐OR allows combination of dependent Common Performance Conditions (CPCs). Finally, a quantitative HEP analysis is applied to enable more precise estimation of HEP through a probabilistic approach. Copyright © 2016 John Wiley & Sons, Ltd.

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“…This viewpoint is generally reasonable since the failure of human beings may be caused by their own deficiencies such as human fatigue . This THERP technique has been verified to be a useful tool when it was applied to human reliability analysis in maritime transportation recently, and it was also introduced to validate other human reliability analysis techniques …”

Section: Introductionmentioning

confidence: 99%

An Evidential Reasoning‐Based CREAM to Human Reliability Analysis in Maritime Accident Process

et al. 2017

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This article proposes a modified cognitive reliability and error analysis method (CREAM) for estimating the human error probability in the maritime accident process on the basis of an evidential reasoning approach. This modified CREAM is developed to precisely quantify the linguistic variables of the common performance conditions and to overcome the problem of ignoring the uncertainty caused by incomplete information in the existing CREAM models. Moreover, this article views maritime accident development from the sequential perspective, where a scenario- and barrier-based framework is proposed to describe the maritime accident process. This evidential reasoning-based CREAM approach together with the proposed accident development framework are applied to human reliability analysis of a ship capsizing accident. It will facilitate subjective human reliability analysis in different engineering systems where uncertainty exists in practice.

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Estimating Human Error Probability using a modified CREAM

Cited by 48 publications

References 10 publications

The Management of Uncertainty: Model for Evaluation of Human Error Probability in Railway System

The Management of Uncertainty: Model for Evaluation of Human Error Probability in Railway System

A Bayesian Network to Ease Knowledge Acquisition of Causal Dependence in CREAM: Application of Recursive Noisy‐OR Gates

An Evidential Reasoning‐Based CREAM to Human Reliability Analysis in Maritime Accident Process

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