An Integrated CREAM for Human Reliability Analysis Based on Consensus Reaching Process under Probabilistic Linguistic Environment

Author:

Xu Xue-Guo1,Zhang Ling1,Wang Si-Xuan2,Gong Hua-Ping3ORCID,Liu Hu-Chen2

Affiliation:

1. School of Management, Shanghai University, Shanghai 200444, China

2. School of Economics and Management, Tongji University, Shanghai 200092, China

3. School of Foreign Languages, Tongji University, Shanghai 200092, China

Abstract

Human reliability analysis (HRA) is widely used to evaluate the impact of human errors on various complex human–machine systems for enhancing their safety and reliability. Nevertheless, it is hard to estimate the human error probability (HEP) in reality due to the uncertainty of state assessment information and the complex relations among common performance conditions (CPCs). In this paper, we aim to present a new integrated cognitive reliability and error analysis method (CREAM) to solve the HRA problems under probabilistic linguistic environment. First, the probabilistic linguistic term sets (PLTSs) are utilized to handle the uncertain task state assessments provided by experts. Second, the minimum conflict consensus model (MCCM) is employed to deal with conflict task state assessment information to assist experts reach consensus. Third, the entropy weighting method is used to determine the relative objective weights of CPCs. Additionally, the CPC effect indexes are introduced to assess the overall effect of CPCs on performance reliability and obtain the HEP estimation. Finally, the reliability of the proposed CREAM is demonstrated via a healthcare practical case. The result shows that the new integrated CREAM can not only effectively represent experts’ uncertain task state assessments but also determine more reliable HEP estimation in HRA.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

MDPI AG

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