Abstract
A literature review of a number of Human Reliability Analysis (HRA) methodologies is carried out in this paper. The focus of the paper is on the use of an HRA method to quantify the probability of a human error that can then be plugged into the overall plant Probabilistic Risk Assessment (PRA), specifically in the nuclear industry. In keeping with this criterion, the modeling techniques selected for review are Technique for Human Error Rate Prediction (THERP), Accident Sequence Evaluation Program (ASEP), Cause-Based Decision Tree Method (CBDTM), Human Cognitive Reliability/Operator Reliability Experiments (HCR/ORE), Simplified Plant Analysis Risk-Human (SPAR-H) reliability assessment, Justified Human Error Data Information (JHEDI), Cognitive Reliability and Error Analysis Method (CREAM), A Technique for Human Error Analysis (ATHEANA), Nuclear Action Reliability Assessment (NARA) and Human Error Assessment and Reduction Technique (HEART). It is concluded that while no one methodology can cover all aspects of HRA due to each having their own set of limitations, a combination of certain methodologies can provide a more accurate Human Error Probability (HEP) for input into the PRA.
Keywords
- Human reliability analysis
- Nuclear engineering
- Probabilistic risk assessment
- Technique for human error rate prediction
- Accident sequence evaluation program
- Cause-Based decision tree method
- Human cognitive reliability/operator reliability experiments
- Simplified plant analysis risk-human (SPAR-H) reliability assessment
- Justified human error data information
- Cognitive reliability and error analysis method
- A technique for human error analysis
- Nuclear action reliability assessment
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Dsouza, N., Lu, L. (2017). A Literature Review on Human Reliability Analysis Techniques Applied for Probabilistic Risk Assessment in the Nuclear Industry. In: Cetiner, S., Fechtelkotter, P., Legatt, M. (eds) Advances in Human Factors in Energy: Oil, Gas, Nuclear and Electric Power Industries. Advances in Intelligent Systems and Computing, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-319-41950-3_4
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