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首页> 外文期刊>Industrial & Engineering Chemistry Research >Use of Fuzzy Cause-Effect Digraph for Resolution Fault Diagnosis lor Process Plants. 2. Diagnostic Algorithm and Applications
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Use of Fuzzy Cause-Effect Digraph for Resolution Fault Diagnosis lor Process Plants. 2. Diagnostic Algorithm and Applications

机译:使用模糊因果图来解决过程工厂的故障诊断。 2.诊断算法与应用

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A new model graph called the fuzzy cause—effect digraph (FCDG) model was already proposed in part 1, and its capability to eliminate spurious interpretations attributed to system compensation and inverse responses from backward loops and forward paths is to be demonstrated. In this paper we attempt to develop a new fault diagnosis algorithm based on the fuzzy cause—effect digraph model. This method applies fuzzy reasoning to estimate the states of unmeasured variables, to explain fault propagation paths, and to locate fault origins. In particular, it can obtain the fault origin occurring in the process with single and multiple loops at the early stage of fault. This study uses a CSTR as an example to explicate this diagnosis method and compares the results with those of other methods.
机译:在第1部分中,已经提出了一种新的模型图,称为模糊因果图(FCDG)模型,该模型图具有消除系统补偿引起的虚假解释以及来自后向回路和前向路径的逆响应的能力。在本文中,我们尝试开发一种基于模糊因果图模型的新故障诊断算法。该方法应用模糊推理来估计未测变量的状态,解释故障传播路径并定位故障源。特别是,它可以在故障的早期阶段获得具有单个和多个回路的过程中发生的故障源。本研究以CSTR为例来说明这种诊断方法,并将结果与​​其他方法进行比较。

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