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Applying fuzzy cognitive-maps knowledge-representation to failure modes effects analysis

机译:模糊认知图知识表示在失效模式影响分析中的应用

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A failure mode and effects analysis (FMEA) seeks to determine how a system will behave in the event of a device failure. It involves the integration of several expert tasks to select components for analysis, determine failure modes, predict failure effects, propose corrective actions, etc. During an FMEA, numerical values are often not available or applicable and qualitative thresholds and linguistic terms such as high, slightly high, low, etc., are usually more relevant to the design than numerical expressions. Fuzzy set theory and fuzzy cognitive maps provide a basis for automating much of the reasoning required to carry out an FMEA on a system. They offer a suitable technique to allow symbolic reasoning in the FMEA instead of numerical methods, thus providing human like interpretations of the system model under analysis, and they allow for the integration of multiple expert opinions. This paper describes how fuzzy cognitive maps can be used to describe a system, its missions, failure modes, their causes and effects. The maps can then be evaluated using both numerical and graphical methods to determine the effects of a failure and the consistency of design decisions.
机译:故障模式和影响分析(FMEA)旨在确定设备发生故障时系统的行为。它涉及几个专家任务的集成,以选择要分析的组件,确定故障模式,预测故障影响,提出纠正措施等。在FMEA期间,通常不可用或不可用数值以及定性阈值和语言术语,例如高,略高,略低等通常比数字表达式与设计更相关。模糊集理论和模糊认知图为自动化在系统上执行FMEA所需的大部分推理提供了基础。他们提供了一种适合的技术,允许在FMEA中使用符号推理,而不是使用数值方法,从而为分析中的系统模型提供类似于人的解释,并且它们允许集成多个专家意见。本文介绍了如何使用模糊认知图来描述系统,其任务,故障模式及其原因和结果。然后可以使用数字和图形方法对这些图进行评估,以确定故障的影响和设计决策的一致性。

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