New method for dependence assessment in human reliability analysis based on linguistic hesitant fuzzy information
Autor: | Lin-Xiu Hou, Yu-Jie Zhu, Hu-Chen Liu, Ling Zhang |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Systematic error
Best-worst method Computer science 020209 energy Fuzzy set TK9001-9401 Technique for Human Error Rate Prediction Context (language use) 02 engineering and technology Dependence analysis Human reliability analysis Fuzzy logic Linguistics 030218 nuclear medicine & medical imaging THERP technique 03 medical and health sciences 0302 clinical medicine Nuclear Energy and Engineering 0202 electrical engineering electronic engineering information engineering Key (cryptography) Dependence assessment Nuclear engineering. Atomic power Linguistic hesitant fuzzy set Human reliability |
Zdroj: | Nuclear Engineering and Technology, Vol 53, Iss 11, Pp 3675-3684 (2021) |
ISSN: | 1738-5733 |
Popis: | Human reliability analysis (HRA) is a proactive approach to model and evaluate human systematic errors, and has been extensively applied in various complicated systems. Dependence assessment among human errors plays a key role in the HRA, which relies heavily on the knowledge and experience of experts in real-world cases. Moreover, there are ofthen different types of uncertainty when experts use linguistic labels to evaluate the dependencies between human failure events. In this context, this paper aims to develop a new method based on linguistic hesitant fuzzy sets and the technique for human error rate prediction (THERP) technique to manage the dependence in HRA. This method handles the linguistic assessments given by experts according to the linguistic hesitant fuzzy sets, determines the weights of influential factors by an extended best-worst method, and confirms the degree of dependence between successive actions based on the THERP method. Finally, the effectiveness and practicality of the presented linguistic hesitant fuzzy THERP method are demonstrated through an empirical healthcare dependence analysis. |
Databáze: | OpenAIRE |
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