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Fuzzy temporal fault tree analysis of dynamic systems

机译:动态系统的模糊时间故障树分析

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Fault tree analysis (FTA) is a powerful technique that is widely used for evaluating system safety and reliability. It can be used to assess the effects of combinations of failures on system behaviour but is unable to capture sequence dependent dynamic behaviour. A number of extensions to fault trees have been proposed to overcome this limitation. Pandora, one such extension, introduces temporal gates and temporal laws to allow dynamic analysis of temporal fault trees (TFTs). It can be easily integrated in model-based design and analysis techniques. The quantitative evaluation of failure probability in Pandora TFTs is performed using exact probabilistic data about component failures. However, exact data can often be difficult to obtain. In this paper, we propose a method that combines expert elicitation and fuzzy set theory with Pandora TFTs to enable dynamic analysis of complex systems with limited or absent exact quantitative data. This gives Pandora the ability to perform quantitative analysis under uncertainty, which increases further its potential utility in the emerging field of model-based design and dependability analysis. The method has been demonstrated by applying it to a fault tolerant fuel distribution system of a ship, and the results are compared with the results obtained by other existing techniques. (C) 2016 Elsevier Inc. All rights reserved.
机译:故障树分析(FTA)是一项功能强大的技术,已广泛用于评估系统安全性和可靠性。它可用于评估故障组合对系统行为的影响,但无法捕获依赖序列的动态行为。已经提出了对故障树的许多扩展来克服该限制。 Pandora是这种扩展之一,它引入了时间门和时间定律,以允许对时间故障树(TFT)进行动态分析。它可以轻松地集成到基于模型的设计和分析技术中。使用有关组件故障的准确概率数据对Pandora TFT中的故障概率进行定量评估。但是,通常很难获得准确的数据。在本文中,我们提出了一种将专家启发和模糊集理论与Pandora TFT结合使用的方法,可以对有限或缺少精确定量数据的复杂系统进行动态分析。这使Pandora能够在不确定性下进行定量分析,从而进一步提高了其在基于模型的设计和可靠性分析等新兴领域中的潜在效用。该方法已通过将其应用于船舶的容错燃料分配系统进行了验证,并将结果与​​其他现有技术获得的结果进行了比较。 (C)2016 Elsevier Inc.保留所有权利。

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