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SDG-based fault diagnosis and application based on reasoning method of granular computing

机译:基于SDG的故障诊断方法及基于粒度计算的推理方法

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Signed directed graph (SDG) as a qualitative model is used in fault diagnosis, because it can express causal relationships among variables of large-scale complex industry systems. However, many relevant results are included in diagnostic conclusions leading to low resolution in based-SDG fault diagnosis, so to solve this problem, granule is used to formally express the elements of SDG model in this paper, after that granular base containing knowledge which reflects the causal relation of faults and symptoms is constructed. And a searching and reasoning method based on granule is used in searching of fault source, consequently fault source is obtained by searching granular base and computing the most similarity. So the resolution could be improved. A 65t/h steam boiler system is taken as an example in the paper, and its answer show the method is feasible.
机译:有符号有向图(SDG)作为定性模型用于故障诊断,因为它可以表示大型复杂工业系统变量之间的因果关系。然而,诊断结论中包含许多相关结果,导致基于SDG的故障诊断的分辨率较低,因此为了解决该问题,本文使用了颗粒形式来正式表达SDG模型的要素,之后该颗粒状基础包含了能够反映出相关知识的知识。建立了故障和症状之间的因果关系。在故障源的搜索中采用了基于颗粒的搜索推理方法,从而通过搜索粒度库并计算出最相似度来获得故障源。因此,可以提高分辨率。以65t / h蒸汽锅炉为例,其回答表明该方法是可行的。

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