Quantitative Risk Assessment of a Large-Scale Central Spherical Detector System by an Improved Fuzzy Fault Tree Analysis

Runze Tan, Xiaobin Li, Jingxia Yue, Zhipeng Du and Harish Garg

Mathematical Problems in Engineering, 2022, vol. 2022, 1-19

Abstract: Central spherical detector is one of the most essential and complicated support systems for the large-scale underground neutrino observatory. If a central spherical detector accident happens, the consequences may be disastrous with huge economic loss. However, there is very few published research works which focus on its quantitative risk analysis. In this paper, an improved fuzzy fault tree analysis (FFTA) method incorporated with the weakest t-norm (Tω) algorithm, the confidence level concept, and the analytic hierarchy process (AHP) approach is proposed to perform its risk assessment. By carrying out the identification of failure modes and failure reasons, fault tree (FT) model of central detector is constructed based on logical relationship among subcomponents. Fuzzy set theory is applied to obtain failure data, and Tω algorithm is exploited to eliminate fuzzy accumulation in the aggregation process. In addition, a confidence level coefficient and AHP approach are employed to enhance the reliability of the evaluation. Both importance and sensitivity analysis have been conducted to identify the critical basic events and provide improvement measures. Finally, the comparison of the occurrence possibility of detector failure is used to verify the applicability and the feasibility of proposed method. The calculated results indicate that the improved approach is more consistent with real situation and can provide a more effective engineering reference for the risk decision of central spherical detector.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5104612

DOI: 10.1155/2022/5104612

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