Abstract
BackgroundCognitive Reliability and Error Analysis Method (CREAM), as one of the second-generation methods, has been developed to overcome the shortcomings of the first-generation human reliability analysis methods. Although it is a useful tool for assessing the effects of context on human failure probability, namely common performance conditions (CPCs), there still exist some problems, such as lack of data about CPCs, and their unclear relationship with the operator control mode. ObjectiveThe current paper aimed at applying CREAM Bayesian Network (BN) in a real-world situation in order to identify the limitations associated to CPCs in estimating Human Error Probability (HEP). MethodIn this paper, the data pertaining to CPCs were collected by a self-designed questionnaire. CREAM BN was then performed in a five-step methodology, including the identification of the primary effects of CPCs, adjustment of dependency of CPCs, new grouping of CPCs, determination of control modes, and HEP calculation. ResultsThe results showed that there are varied values of control modes in CREAM BN in comparison with the basic CREAM. On the other hand, this method provides the grounds for incorporating various importance levels of CPCs in HEP estimation by changing the nature of prior conditional probabilities from the deterministic one into the probabilistic one. ConclusionThe methodology introduced in this study provides a simple method for the calculation of HEP in the complex industries.•This method provides the application of the CREAM BN in a real-environmental in practice.•This method provides a foundation for incorporating various importance levels of the CPCs in the HEP estimation by changing the nature of prior conditional probabilities from deterministic into probabilistic.•It could reduce the uncertainty in the calculation of HEP.
Highlights
Cognitive Reliability and Error Analysis Method (CREAM), as one of the second-generation methods, has been developed to overcome the shortcomings of the first-generation human reliability analysis methods
The results were revealed that the content validity ratio (CVR) for all questions exceeded 0.62
These scores are converted to the fuzzy values using the fuzzy numbers (Table 2) in order to overcome the shortcomings existing in the traditional deterministic CREAM method
Summary
Cognitive Reliability and Error Analysis Method (CREAM), as one of the second-generation methods, has been developed to overcome the shortcomings of the first-generation human reliability analysis methods. It is a useful tool for assessing the effects of context on human failure probability, namely common performance conditions (CPCs), there still exist some problems, such as lack of data about CPCs, and their unclear relationship with the operator control mode. Objective: The current paper aimed at applying CREAM Bayesian Network (BN) in a real-world situation in order to identify the limitations associated to CPCs in estimating Human Error Probability (HEP). CREAM BN was performed in a five-step methodology, including the identification of the primary effects of CPCs, adjustment of dependency of CPCs, new grouping of CPCs, determination of control modes, and HEP calculation. This method provides the grounds for incorporating various importance levels of CPCs in HEP estimation by changing the nature of prior conditional probabilities from the deterministic one into the probabilistic one. Conclusion: The methodology introduced in this study provides a simple method for the calculation of HEP in the complex industries
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