Abstract
Success Likelihood Index Model (SLIM) is one of the widely-used methods in human reliability assessment especially when data is insufficient. However, this method suffers from uncertainty as it heavily relies on expert judgment for determining the model parameters such as the rates and weights of the performance shaping factors. The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling the uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We applied both SLIM and BN-SLIM models to a hypothetical example and compared the results. It is shown that BN-SLIM is able to provide a better estimation of human error probability by considering dependencies. The probability updating feature of BN-SLIM in particular makes it possible to use new information to update the prior beliefs about the rates of the performance shaping factors, thus updating the resultant human error probabilities.
Original language | English |
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Title of host publication | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
Editors | Michael Beer, Enrico Zio |
Place of Publication | Singapore |
Publisher | Research Publishing |
Pages | 309-315 |
Number of pages | 7 |
ISBN (Electronic) | 9789811127243 |
ISBN (Print) | 978-981-11-2724-3 |
DOIs | |
Publication status | Published - 2019 |
Event | 29th European Safety and Reliability Conference - Hannover, Germany Duration: 22 Sept 2019 → 26 Sept 2019 https://esrel2019.org/#/ |
Publication series
Name | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
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Conference
Conference | 29th European Safety and Reliability Conference |
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Abbreviated title | ESREL 2019 |
Country/Territory | Germany |
City | Hannover |
Period | 22/09/19 → 26/09/19 |
Internet address |
Keywords
- Human error probability
- Uncertainty
- Bayesian network
- Success likelihood index model