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首页> 外文期刊>Transactions of The Institution of Chemical Engineers. Process Safety and Environmental Protection, Part B >Failure mode effect and criticality analysis using dempster shafer theory and its comparison with fuzzy failure mode effect and criticality analysis: A case study applied to LNG storage facility
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Failure mode effect and criticality analysis using dempster shafer theory and its comparison with fuzzy failure mode effect and criticality analysis: A case study applied to LNG storage facility

机译:失效模式效应和关键性分析使用Dempster Shafer理论及其与模糊失效模式效应和关键性分析的比较 - 以LNG储存设施应用的案例研究

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摘要

Failure mode effects and criticality analysis (FMECA) is widely used, by developing a Risk Priority Number (RPN), to identify the failure modes and to prioritize them. But this has been extensively criticized due to several drawbacks in the literature. This issue can be solved partly by using Fuzzy FMECA (FFMECA) although Fuzzy logic itself has been criticized of having a direct bearing on subjectivity. This paper makes use of Dempster-Shafer Theory (DST) of evidence-a proper mathematical framework to deal with the epistemic uncertainty often affecting the input evaluations of risk parameters. DST based FMECA is capable of providing an appropriate, precise and fault-free, failure mode prioritization. Belief and Plausibility distributions are used to synthesize the obtainable information and to make them useful for the purpose. The results obtained from DST-FMECA is compared to the results drawn from the FFMECA applications (already conducted in the liquefied natural gas (LNG) storage facility) to validate FFMECA and vice versa. The comparative results presented in this paper establish the capabilities of both the approaches, especially in a complex system like the LNG storage and similar facilities where even a minor failure may lead to catastrophic effects. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
机译:通过开发风险优先级(RPN)来识别失败模式并优先考虑它们,广泛使用失败模式效应和临界分析(FMECA)。但由于文献中的几个缺点,这一直受到广泛的批评。可以通过使用模糊FMECA(FFMECA)部分来解决这个问题,尽管模糊逻辑本身被批评导向主体性。本文利用Dempster-Shafer理论(DST)证据 - 一个适当的数学框架来处理经常影响风险参数的输入评估的认知不确定性。基于DST的FMECA能够提供适当,精确和无故障,失效模式优先级。信仰和合理性分布用于综合可获得的信息,并使它们为目的有用。将从DST-FMECA获得的结果与FFMECA应用得出的结果进行比较(已经在液化天然气(LNG)存储设施中进行)以验证FFMECA,反之亦然。本文提出的比较结果确定了这种方法的能力,特别是在液化天然气储存和类似设施中的复杂系统中,甚至轻微失败可能导致灾难性的影响。 (c)2020化学工程师机构。 elsevier b.v出版。保留所有权利。

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